diff --git a/Countries_of_the_world/Countries of the world analysis.ipynb b/Countries_of_the_world/Countries of the world analysis.ipynb index daf45b6..e51546b 100644 --- a/Countries_of_the_world/Countries of the world analysis.ipynb +++ b/Countries_of_the_world/Countries of the world analysis.ipynb @@ -1,3253 +1,2159 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Countries of the world\n", - "\n", - "- Updated to GDS 2.0 version\n", - "- Link to original blog post: https://towardsdatascience.com/community-detection-of-the-countries-of-the-world-with-neo4j-graph-data-science-4d3a022f8399" - ], - "metadata": { - "id": "qugkv-nB4gc3" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install neo4j" - ], - "metadata": { - "id": "8-RMR2Nv4fiD", - "outputId": "208d660f-1069-4e80-b541-a3fd340b1d32", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting neo4j\n", - " Downloading neo4j-4.4.2.tar.gz (89 kB)\n", - "\u001b[?25l\r\u001b[K |███▋ | 10 kB 19.4 MB/s eta 0:00:01\r\u001b[K |███████▎ | 20 kB 25.4 MB/s eta 0:00:01\r\u001b[K |███████████ | 30 kB 13.4 MB/s eta 0:00:01\r\u001b[K |██████████████▋ | 40 kB 10.3 MB/s eta 0:00:01\r\u001b[K |██████████████████▎ | 51 kB 6.1 MB/s eta 0:00:01\r\u001b[K |██████████████████████ | 61 kB 7.2 MB/s eta 0:00:01\r\u001b[K |█████████████████████████▋ | 71 kB 7.7 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▎ | 81 kB 7.4 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 89 kB 1.3 MB/s \n", - "\u001b[?25hRequirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j) (2018.9)\n", - "Building wheels for collected packages: neo4j\n", - " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for neo4j: filename=neo4j-4.4.2-py3-none-any.whl size=115365 sha256=43c3ddc1dbfa97e8aa3c34730811c26ad1d4e51d904fffe4d9c5c3b838379b45\n", - " Stored in directory: /root/.cache/pip/wheels/10/d6/28/95029d7f69690dbc3b93e4933197357987de34fbd44b50a0e4\n", - "Successfully built neo4j\n", - "Installing collected packages: neo4j\n", - "Successfully installed neo4j-4.4.2\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "I recommend you setup [a blank project on Neo4j Sandbox environment](https://sandbox.neo4j.com/?usecase=blank-sandbox), but you can also use other environment versions" - ], - "metadata": { - "id": "drvMogZL5Ex6" - } - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "5DeXOj_o25i8" - }, - "outputs": [], - "source": [ - "# Define Neo4j connections\n", - "from neo4j import GraphDatabase\n", - "host = 'bolt://3.235.2.228:7687'\n", - "user = 'neo4j'\n", - "password = 'seats-drunks-carbon'\n", - "driver = GraphDatabase.driver(host,auth=(user, password))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "iSosOYR-25jA" - }, - "outputs": [], - "source": [ - "def drop_graph(name):\n", - " with driver.session() as session:\n", - " drop_graph_query = \"\"\"\n", - " CALL gds.graph.drop('{}');\n", - " \"\"\".format(name)\n", - " session.run(drop_graph_query)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "9ekQ13NM25jA" - }, - "outputs": [], - "source": [ - "# Import libraries\n", - "import pandas as pd\n", - "\n", - "def read_query(query, params={}):\n", - " with driver.session() as session:\n", - " result = session.run(query, params)\n", - " return pd.DataFrame([r.values() for r in result], columns=result.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UKpLZvAj25jB" - }, - "source": [ - "### Graph schema\n", - "We will be using the Countries of the world dataset made available on Kaggle by Fernando Lasso. Looking at the acknowledgments, the data originates from the CIA's World Factbook. Unfortunately, the contributor did not provide the year the dataset was compiled. My guess is the year 2013, but I might be wrong. The dataset contains various metrics such as area size, population, infant mortality, and more about 227 countries of the world." - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qugkv-nB4gc3" + }, + "source": [ + "# Countries of the world\n", + "\n", + "- Updated to GDS 2.3 version and Neo4j v5\n", + "- Link to original blog post: https://towardsdatascience.com/community-detection-of-the-countries-of-the-world-with-neo4j-graph-data-science-4d3a022f8399" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "8-RMR2Nv4fiD", + "outputId": "208d660f-1069-4e80-b541-a3fd340b1d32" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "PyDKm5dR25jD" - }, - "source": [ - "### Graph import\n", - "\n", - "For some reason, the numbers in the CSV file use a comma as a floating point instead of a dot (0,1 instead of 0.1). We need to preprocess the data to be able to cast the numbers to float in Neo4j. With the help of an APOC procedure apoc.cypher.run, we can preprocess and store the data in a single cypher query. apoc.cypher.run allows us to run independent subqueries within the main cypher query and is excellent for various use cases." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting neo4j\n", + " Downloading neo4j-4.4.2.tar.gz (89 kB)\n", + "\u001b[?25l\r", + "\u001b[K |███▋ | 10 kB 19.4 MB/s eta 0:00:01\r", + "\u001b[K |███████▎ | 20 kB 25.4 MB/s eta 0:00:01\r", + "\u001b[K |███████████ | 30 kB 13.4 MB/s eta 0:00:01\r", + "\u001b[K |██████████████▋ | 40 kB 10.3 MB/s eta 0:00:01\r", + "\u001b[K |██████████████████▎ | 51 kB 6.1 MB/s eta 0:00:01\r", + "\u001b[K |██████████████████████ | 61 kB 7.2 MB/s eta 0:00:01\r", + "\u001b[K |█████████████████████████▋ | 71 kB 7.7 MB/s eta 0:00:01\r", + "\u001b[K |█████████████████████████████▎ | 81 kB 7.4 MB/s eta 0:00:01\r", + "\u001b[K |████████████████████████████████| 89 kB 1.3 MB/s \n", + "\u001b[?25hRequirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j) (2018.9)\n", + "Building wheels for collected packages: neo4j\n", + " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for neo4j: filename=neo4j-4.4.2-py3-none-any.whl size=115365 sha256=43c3ddc1dbfa97e8aa3c34730811c26ad1d4e51d904fffe4d9c5c3b838379b45\n", + " Stored in directory: /root/.cache/pip/wheels/10/d6/28/95029d7f69690dbc3b93e4933197357987de34fbd44b50a0e4\n", + "Successfully built neo4j\n", + "Installing collected packages: neo4j\n", + "Successfully installed neo4j-4.4.2\n" + ] + } + ], + "source": [ + "!pip install neo4j" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "drvMogZL5Ex6" + }, + "source": [ + "I recommend you setup [a blank project on Neo4j Sandbox environment](https://sandbox.neo4j.com/?usecase=blank-sandbox), but you can also use other environment versions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "5DeXOj_o25i8" + }, + "outputs": [], + "source": [ + "# Define Neo4j connections\n", + "from neo4j import GraphDatabase\n", + "host = 'bolt://3.231.25.240:7687'\n", + "user = 'neo4j'\n", + "password = 'hatchets-visitor-axes'\n", + "driver = GraphDatabase.driver(host,auth=(user, password))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "iSosOYR-25jA" + }, + "outputs": [], + "source": [ + "def drop_graph(name):\n", + " with driver.session() as session:\n", + " drop_graph_query = \"\"\"\n", + " CALL gds.graph.drop('{}');\n", + " \"\"\".format(name)\n", + " session.run(drop_graph_query)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "9ekQ13NM25jA" + }, + "outputs": [], + "source": [ + "# Import libraries\n", + "import pandas as pd\n", + "\n", + "def read_query(query, params={}):\n", + " with driver.session() as session:\n", + " result = session.run(query, params)\n", + " return pd.DataFrame([r.values() for r in result], columns=result.keys())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UKpLZvAj25jB" + }, + "source": [ + "### Graph schema\n", + "We will be using the Countries of the world dataset made available on Kaggle by Fernando Lasso. Looking at the acknowledgments, the data originates from the CIA's World Factbook. Unfortunately, the contributor did not provide the year the dataset was compiled. My guess is the year 2013, but I might be wrong. The dataset contains various metrics such as area size, population, infant mortality, and more about 227 countries of the world." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PyDKm5dR25jD" + }, + "source": [ + "### Graph import\n", + "\n", + "For some reason, the numbers in the CSV file use a comma as a floating point instead of a dot (0,1 instead of 0.1). We need to preprocess the data to be able to cast the numbers to float in Neo4j. With the help of an APOC procedure apoc.cypher.run, we can preprocess and store the data in a single cypher query. apoc.cypher.run allows us to run independent subqueries within the main cypher query and is excellent for various use cases." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49 }, + "id": "z93mpqXv25jD", + "outputId": "6ce3ebf0-aa95-4c10-d87d-690a3362c017" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "z93mpqXv25jD", - "outputId": "6ce3ebf0-aa95-4c10-d87d-690a3362c017", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: []" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "import_query = \"\"\"\n", - "\n", - "LOAD CSV WITH HEADERS FROM \"https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/countries_of_the_world.csv\" as row\n", - "// cleanup the data and replace comma floating point with a dot\n", - "CALL apoc.cypher.run(\n", - " \"UNWIND keys($row) as key \n", - " WITH row,\n", - " key,\n", - " toFloat(replace(row[key],',','.')) as clean_value\n", - " // exclude string properties\n", - " WHERE NOT key in ['Country','Region'] \n", - " RETURN collect([key,clean_value]) as keys\", \n", - " {row:row}) YIELD value\n", - "MERGE (c:Country{name:trim(row.Country)})\n", - "SET c+= apoc.map.fromPairs(value.keys)\n", - "MERGE (r:Region{name:trim(row.Region)})\n", - "MERGE (c)-[:PART_OF]->(r)\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(import_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3J7I1k7b25jF" - }, - "source": [ - "### Identify missing values\n", - "Another useful APOC procedure is apoc.meta.nodeTypeProperties. With it, we can examine the node property schema of the graph. We will use it to identify how many missing values each feature of the country has." + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import_query = \"\"\"\n", + "\n", + "LOAD CSV WITH HEADERS FROM \"https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/countries_of_the_world.csv\" as row\n", + "// cleanup the data and replace comma floating point with a dot\n", + "CALL apoc.cypher.run(\n", + " \"UNWIND keys($row) as key \n", + " WITH row,\n", + " key,\n", + " toFloat(replace(row[key],',','.')) as clean_value\n", + " // exclude string properties\n", + " WHERE NOT key in ['Country','Region'] \n", + " RETURN collect([key,clean_value]) as keys\", \n", + " {row:row}) YIELD value\n", + "MERGE (c:Country{name:trim(row.Country)})\n", + "SET c+= apoc.map.fromPairs(value.keys)\n", + "MERGE (r:Region{name:trim(row.Region)})\n", + "MERGE (c)-[:PART_OF]->(r)\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(import_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3J7I1k7b25jF" + }, + "source": [ + "### Identify missing values\n", + "Another useful APOC procedure is apoc.meta.nodeTypeProperties. With it, we can examine the node property schema of the graph. We will use it to identify how many missing values each feature of the country has." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 }, + "id": "c4pDpXio25jF", + "outputId": "9b17ec6d-3e34-4f98-fd6a-de7a4141e702" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "c4pDpXio25jF", - "outputId": "9b17ec6d-3e34-4f98-fd6a-de7a4141e702", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " propertyName missing_value pct_missing_value\n", - "0 Climate 22 0.096916\n", - "1 Literacy (%) 18 0.079295\n", - "2 Industry 16 0.070485\n", - "3 Agriculture 15 0.066079\n", - "4 Service 15 0.066079\n", - "5 Phones (per 1000) 4 0.017621\n", - "6 Deathrate 4 0.017621\n", - "7 Net migration 3 0.013216\n", - "8 Infant mortality (per 1000 births) 3 0.013216\n", - "9 Birthrate 3 0.013216" - ], - "text/html": [ - "\n", - "
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propertyNamemissing_valuepct_missing_value
0Climate220.096916
1Literacy (%)180.079295
2Industry160.070485
3Agriculture150.066079
4Service150.066079
5Phones (per 1000)40.017621
6Deathrate40.017621
7Net migration30.013216
8Infant mortality (per 1000 births)30.013216
9Birthrate30.013216
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propertyNamemissing_valuepct_missing_value
0Climate220.096916
1Literacy (%)180.079295
2Industry160.070485
3Agriculture150.066079
4Service150.066079
5Phones (per 1000)40.017621
6Deathrate40.017621
7Net migration30.013216
8Infant mortality (per 1000 births)30.013216
9Birthrate30.013216
\n", + "
" ], - "source": [ - "identify_missing_values_query = \"\"\"\n", - "\n", - "// Only look at properties of nodes labeled \"Country\"\n", - "CALL apoc.meta.nodeTypeProperties({labels:['Country']})\n", - "YIELD propertyName, propertyObservations, totalObservations\n", - "RETURN propertyName,\n", - " (totalObservations - propertyObservations) as missing_value,\n", - " (totalObservations - propertyObservations) / toFloat(totalObservations) as pct_missing_value\n", - "ORDER BY pct_missing_value DESC LIMIT 10\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(identify_missing_values_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "V858FLaB25jG" - }, - "source": [ - "It looks like we don't have many missing values. However, we will disregard features with more than four missing values from our further analysis for the sake of simplicity.\n", - "### High correlation filter\n", - "High correlation filter is a simple data dimensionality reduction technique. Features with high correlation are likely to carry similar information and are more linearly dependant. Using multiple features with related information can bring down the performance of various models and can be avoided by dropping one of the two correlating features." + "text/plain": [ + " propertyName missing_value pct_missing_value\n", + "0 Climate 22 0.096916\n", + "1 Literacy (%) 18 0.079295\n", + "2 Industry 16 0.070485\n", + "3 Agriculture 15 0.066079\n", + "4 Service 15 0.066079\n", + "5 Phones (per 1000) 4 0.017621\n", + "6 Deathrate 4 0.017621\n", + "7 Net migration 3 0.013216\n", + "8 Infant mortality (per 1000 births) 3 0.013216\n", + "9 Birthrate 3 0.013216" ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identify_missing_values_query = \"\"\"\n", + "\n", + "// Only look at properties of nodes labeled \"Country\"\n", + "CALL apoc.meta.nodeTypeProperties({labels:['Country']})\n", + "YIELD propertyName, propertyObservations, totalObservations\n", + "RETURN propertyName,\n", + " (totalObservations - propertyObservations) as missing_value,\n", + " (totalObservations - propertyObservations) / toFloat(totalObservations) as pct_missing_value\n", + "ORDER BY pct_missing_value DESC LIMIT 10\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(identify_missing_values_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V858FLaB25jG" + }, + "source": [ + "It looks like we don't have many missing values. However, we will disregard features with more than four missing values from our further analysis for the sake of simplicity.\n", + "### High correlation filter\n", + "High correlation filter is a simple data dimensionality reduction technique. Features with high correlation are likely to carry similar information and are more linearly dependant. Using multiple features with related information can bring down the performance of various models and can be avoided by dropping one of the two correlating features." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 }, + "id": "avNWFhF925jH", + "outputId": "b7c88d8a-9f54-4884-c95a-4b73cb6c21a2" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "avNWFhF925jH", - "outputId": "b7c88d8a-9f54-4884-c95a-4b73cb6c21a2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " feature compare_feature \\\n", - "0 Birthrate Infant mortality (per 1000 births) \n", - "1 GDP ($ per capita) Phones (per 1000) \n", - "2 Deathrate Infant mortality (per 1000 births) \n", - "3 Area (sq. mi.) Population \n", - "4 Birthrate Deathrate \n", - "5 GDP ($ per capita) Net migration \n", - "6 Coastline (coast/area ratio) Crops (%) \n", - "7 Phones (per 1000) Pop. Density (per sq. mi.) \n", - "8 Coastline (coast/area ratio) Pop. Density (per sq. mi.) \n", - "9 Net migration Phones (per 1000) \n", - "\n", - " correlation \n", - "0 0.841210 \n", - "1 0.828151 \n", - "2 0.661350 \n", - "3 0.469985 \n", - "4 0.420948 \n", - "5 0.381256 \n", - "6 0.338594 \n", - "7 0.280954 \n", - "8 0.241690 \n", - "9 0.236930 " - ], - "text/html": [ - "\n", - "
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featurecompare_featurecorrelation
0BirthrateInfant mortality (per 1000 births)0.841210
1GDP ($ per capita)Phones (per 1000)0.828151
2DeathrateInfant mortality (per 1000 births)0.661350
3Area (sq. mi.)Population0.469985
4BirthrateDeathrate0.420948
5GDP ($ per capita)Net migration0.381256
6Coastline (coast/area ratio)Crops (%)0.338594
7Phones (per 1000)Pop. Density (per sq. mi.)0.280954
8Coastline (coast/area ratio)Pop. Density (per sq. mi.)0.241690
9Net migrationPhones (per 1000)0.236930
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featurecompare_featurecorrelation
0BirthrateInfant mortality (per 1000 births)0.841210
1GDP ($ per capita)Phones (per 1000)0.828151
2DeathrateInfant mortality (per 1000 births)0.661350
3Area (sq. mi.)Population0.469985
4BirthrateDeathrate0.420948
5GDP ($ per capita)Net migration0.381256
6Coastline (coast/area ratio)Crops (%)0.338594
7Phones (per 1000)Pop. Density (per sq. mi.)0.280954
8Coastline (coast/area ratio)Pop. Density (per sq. mi.)0.241690
9Net migrationPhones (per 1000)0.236930
\n", + "
" ], - "source": [ - "high_correlation_query = \"\"\"\n", - "\n", - "// Only look at properties of nodes labeled \"Country\"\n", - "CALL apoc.meta.nodeTypeProperties({labels:['Country']})\n", - "YIELD propertyName, propertyObservations, totalObservations\n", - "WITH propertyName,\n", - " (totalObservations - propertyObservations) as missing_value\n", - "// filter our features with more than 5 missing values\n", - "WHERE missing_value < 5 AND propertyName <> 'name'\n", - "WITH collect(propertyName) as features\n", - "MATCH (c:Country)\n", - "UNWIND features as feature\n", - "UNWIND features as compare_feature\n", - "WITH feature,\n", - " compare_feature,\n", - " collect(coalesce(c[feature],0)) as vector_1,\n", - " collect(coalesce(c[compare_feature],0)) as vector_2\n", - "// avoid comparing with a feature with itself\n", - "WHERE feature < compare_feature\n", - "RETURN feature,\n", - " compare_feature,\n", - " gds.similarity.pearson(vector_1, vector_2) AS correlation\n", - "ORDER BY correlation DESC LIMIT 10\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(high_correlation_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KqcIxTJP25jI" - }, - "source": [ - "Interesting to see that birth rate and infant mortality are very correlated. The death rate is also quite correlated with infant mortality, so we will drop the birth and death rate but keep the infant mortality. The number of phones and net migration seems to be correlated with the GDP. We will drop them both as well and keep the GDP. We will also cut the population and retain both the area and population density, which carry similar information.\n", - "### Feature statistics\n", - "At this point, we are left with eight features. We will examine their distributions with the apoc.agg.statistics function. It calculates numeric statistics such as minimum, maximum, and percentile ranks for a collection of values." + "text/plain": [ + " feature compare_feature \\\n", + "0 Birthrate Infant mortality (per 1000 births) \n", + "1 GDP ($ per capita) Phones (per 1000) \n", + "2 Deathrate Infant mortality (per 1000 births) \n", + "3 Area (sq. mi.) Population \n", + "4 Birthrate Deathrate \n", + "5 GDP ($ per capita) Net migration \n", + "6 Coastline (coast/area ratio) Crops (%) \n", + "7 Phones (per 1000) Pop. Density (per sq. mi.) \n", + "8 Coastline (coast/area ratio) Pop. Density (per sq. mi.) \n", + "9 Net migration Phones (per 1000) \n", + "\n", + " correlation \n", + "0 0.841210 \n", + "1 0.828151 \n", + "2 0.661350 \n", + "3 0.469985 \n", + "4 0.420948 \n", + "5 0.381256 \n", + "6 0.338594 \n", + "7 0.280954 \n", + "8 0.241690 \n", + "9 0.236930 " ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_correlation_query = \"\"\"\n", + "\n", + "// Only look at properties of nodes labeled \"Country\"\n", + "CALL apoc.meta.nodeTypeProperties({labels:['Country']})\n", + "YIELD propertyName, propertyObservations, totalObservations\n", + "WITH propertyName,\n", + " (totalObservations - propertyObservations) as missing_value\n", + "// filter our features with more than 5 missing values\n", + "WHERE missing_value < 5 AND propertyName <> 'name'\n", + "WITH collect(propertyName) as features\n", + "MATCH (c:Country)\n", + "UNWIND features as feature\n", + "UNWIND features as compare_feature\n", + "WITH feature,\n", + " compare_feature,\n", + " collect(coalesce(c[feature],0)) as vector_1,\n", + " collect(coalesce(c[compare_feature],0)) as vector_2\n", + "// avoid comparing with a feature with itself\n", + "WHERE feature < compare_feature\n", + "RETURN feature,\n", + " compare_feature,\n", + " gds.similarity.pearson(vector_1, vector_2) AS correlation\n", + "ORDER BY correlation DESC LIMIT 10\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(high_correlation_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KqcIxTJP25jI" + }, + "source": [ + "Interesting to see that birth rate and infant mortality are very correlated. The death rate is also quite correlated with infant mortality, so we will drop the birth and death rate but keep the infant mortality. The number of phones and net migration seems to be correlated with the GDP. We will drop them both as well and keep the GDP. We will also cut the population and retain both the area and population density, which carry similar information.\n", + "### Feature statistics\n", + "At this point, we are left with eight features. We will examine their distributions with the apoc.agg.statistics function. It calculates numeric statistics such as minimum, maximum, and percentile ranks for a collection of values." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 }, + "id": "SfothkMb25jI", + "outputId": "62b7fd71-ae25-4aab-95a6-ef9336139876" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "SfothkMb25jI", - "outputId": "62b7fd71-ae25-4aab-95a6-ef9336139876", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 300 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " potential_feature min max mean \\\n", - "0 Other (%) 33.33 100.00 81.64 \n", - "1 Arable (%) 0.00 62.11 13.80 \n", - "2 Crops (%) 0.00 50.68 4.56 \n", - "3 Coastline (coast/area ratio) 0.00 870.66 21.17 \n", - "4 Infant mortality (per 1000 births) 2.29 191.19 35.51 \n", - "5 Pop. Density (per sq. mi.) 0.00 16271.50 379.05 \n", - "6 GDP ($ per capita) 500.00 55100.00 9689.85 \n", - "7 Area (sq. mi.) 2.00 17075200.00 598227.59 \n", - "\n", - " stdev p50 p75 p95 p99 \n", - "0 16.10 85.70 95.44 99.81 100.00 \n", - "1 13.01 10.42 20.00 40.54 55.30 \n", - "2 8.34 1.03 4.44 20.00 45.71 \n", - "3 72.13 0.73 10.32 92.31 310.69 \n", - "4 35.31 20.97 55.51 103.32 143.64 \n", - "5 1656.53 78.80 188.50 838.60 6482.22 \n", - "6 10026.91 5500.03 15700.06 29600.12 37800.25 \n", - "7 1786336.93 86600.50 437074.00 2345424.00 9631424.00 " - ], - "text/html": [ - "\n", - "
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potential_featureminmaxmeanstdevp50p75p95p99
0Other (%)33.33100.0081.6416.1085.7095.4499.81100.00
1Arable (%)0.0062.1113.8013.0110.4220.0040.5455.30
2Crops (%)0.0050.684.568.341.034.4420.0045.71
3Coastline (coast/area ratio)0.00870.6621.1772.130.7310.3292.31310.69
4Infant mortality (per 1000 births)2.29191.1935.5135.3120.9755.51103.32143.64
5Pop. Density (per sq. mi.)0.0016271.50379.051656.5378.80188.50838.606482.22
6GDP ($ per capita)500.0055100.009689.8510026.915500.0315700.0629600.1237800.25
7Area (sq. mi.)2.0017075200.00598227.591786336.9386600.50437074.002345424.009631424.00
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potential_featureminmaxmeanstdevp50p75p95p99
0Other (%)33.33100.0081.6416.1085.7095.4499.81100.00
1Crops (%)0.0050.684.568.341.034.4420.0045.71
2Arable (%)0.0062.1113.8013.0110.4220.0040.5455.30
3Coastline (coast/area ratio)0.00870.6621.1772.130.7310.3292.31310.69
4Infant mortality (per 1000 births)2.29191.1935.5135.3120.9755.51103.32143.64
5Pop. Density (per sq. mi.)0.0016271.50379.051656.5378.80188.50838.606482.22
6GDP ($ per capita)500.0055100.009689.8510026.915500.0315700.0629600.1237800.25
7Area (sq. mi.)2.0017075200.00598227.591786336.9386600.50437074.002345424.009631424.00
\n", + "
" ], - "source": [ - "feature_stats_query = \"\"\"\n", - "\n", - "// define excluded features\n", - "WITH ['name', \n", - " 'Deathrate', \n", - " 'Birthrate',\n", - " 'Phones (per 1000)',\n", - " 'Net migration', \n", - " 'Population'] as excluded_features\n", - "CALL apoc.meta.nodeTypeProperties({labels:['Country']})\n", - "YIELD propertyName, propertyObservations, totalObservations\n", - "WITH propertyName,\n", - " (totalObservations - propertyObservations) as missing_value\n", - "WHERE missing_value < 5 AND \n", - " NOT propertyName in excluded_features\n", - "// Reduce to a single row\n", - "WITH collect(propertyName) as potential_features\n", - "MATCH (c:Country)\n", - "UNWIND potential_features as potential_feature\n", - "WITH potential_feature, \n", - " apoc.agg.statistics(c[potential_feature],\n", - " [0.5,0.75,0.9,0.95,0.99]) as stats\n", - "RETURN potential_feature, \n", - " apoc.math.round(stats.min,2) as min, \n", - " apoc.math.round(stats.max,2) as max, \n", - " apoc.math.round(stats.mean,2) as mean, \n", - " apoc.math.round(stats.stdev,2) as stdev,\n", - " apoc.math.round(stats.`0.5`,2) as p50,\n", - " apoc.math.round(stats.`0.75`,2) as p75,\n", - " apoc.math.round(stats.`0.95`,2) as p95,\n", - " apoc.math.round(stats.`0.99`,2) as p99\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(feature_stats_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IPbg0N1f25jJ" - }, - "source": [ - "The Federated state of Micronesia has the ratio of coast to area at 870, which is pretty impressive. On the other hand, there are a total of 44 countries in the world with zero coastlines. Another fun fact is that Greenland has a population density rounded to 0 per square mile with its 56361 inhabitants and 2166086 square miles. It might be a cool place to perform social distancing.\n", - "We can observe that most of the features appear to be descriptive, except for the Other (%), which is mostly between 80 and 100. Due to the low variance, we will ignore it in our further analysis.\n", - "### Populate the missing values\n", - "We are left with seven features that we are going to use to infer a similarity network between countries. One thing we need to do before that is to populate the missing values. We will use a simple method and fill in the missing values of the features with the average value of the region the country is part of." + "text/plain": [ + " potential_feature min max mean \\\n", + "0 Other (%) 33.33 100.00 81.64 \n", + "1 Crops (%) 0.00 50.68 4.56 \n", + "2 Arable (%) 0.00 62.11 13.80 \n", + "3 Coastline (coast/area ratio) 0.00 870.66 21.17 \n", + "4 Infant mortality (per 1000 births) 2.29 191.19 35.51 \n", + "5 Pop. Density (per sq. mi.) 0.00 16271.50 379.05 \n", + "6 GDP ($ per capita) 500.00 55100.00 9689.85 \n", + "7 Area (sq. mi.) 2.00 17075200.00 598227.59 \n", + "\n", + " stdev p50 p75 p95 p99 \n", + "0 16.10 85.70 95.44 99.81 100.00 \n", + "1 8.34 1.03 4.44 20.00 45.71 \n", + "2 13.01 10.42 20.00 40.54 55.30 \n", + "3 72.13 0.73 10.32 92.31 310.69 \n", + "4 35.31 20.97 55.51 103.32 143.64 \n", + "5 1656.53 78.80 188.50 838.60 6482.22 \n", + "6 10026.91 5500.03 15700.06 29600.12 37800.25 \n", + "7 1786336.93 86600.50 437074.00 2345424.00 9631424.00 " ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_stats_query = \"\"\"\n", + "\n", + "// define excluded features\n", + "WITH ['name', \n", + " 'Deathrate', \n", + " 'Birthrate',\n", + " 'Phones (per 1000)',\n", + " 'Net migration', \n", + " 'Population'] as excluded_features\n", + "CALL apoc.meta.nodeTypeProperties({labels:['Country']})\n", + "YIELD propertyName, propertyObservations, totalObservations\n", + "WITH propertyName,\n", + " (totalObservations - propertyObservations) as missing_value\n", + "WHERE missing_value < 5 AND \n", + " NOT propertyName in excluded_features\n", + "// Reduce to a single row\n", + "WITH collect(propertyName) as potential_features\n", + "MATCH (c:Country)\n", + "UNWIND potential_features as potential_feature\n", + "WITH potential_feature, \n", + " apoc.agg.statistics(c[potential_feature],\n", + " [0.5,0.75,0.9,0.95,0.99]) as stats\n", + "RETURN potential_feature, \n", + " round(stats.min,2) as min, \n", + " round(stats.max,2) as max, \n", + " round(stats.mean,2) as mean, \n", + " round(stats.stdev,2) as stdev,\n", + " round(stats.`0.5`,2) as p50,\n", + " round(stats.`0.75`,2) as p75,\n", + " round(stats.`0.95`,2) as p95,\n", + " round(stats.`0.99`,2) as p99\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(feature_stats_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IPbg0N1f25jJ" + }, + "source": [ + "The Federated state of Micronesia has the ratio of coast to area at 870, which is pretty impressive. On the other hand, there are a total of 44 countries in the world with zero coastlines. Another fun fact is that Greenland has a population density rounded to 0 per square mile with its 56361 inhabitants and 2166086 square miles. It might be a cool place to perform social distancing.\n", + "We can observe that most of the features appear to be descriptive, except for the Other (%), which is mostly between 80 and 100. Due to the low variance, we will ignore it in our further analysis.\n", + "### Populate the missing values\n", + "We are left with seven features that we are going to use to infer a similarity network between countries. One thing we need to do before that is to populate the missing values. We will use a simple method and fill in the missing values of the features with the average value of the region the country is part of." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "b3r7_G9925jJ", + "outputId": "22ba09d6-ff26-40a0-c4c2-5d4b99aade6e" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "b3r7_G9925jJ", - "outputId": "22ba09d6-ff26-40a0-c4c2-5d4b99aade6e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " 'missing values populated'\n", - "0 missing values populated" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "populate_missing_values = \"\"\"\n", - "\n", - "UNWIND [\"Arable (%)\",\n", - " \"Crops (%)\",\n", - " \"Infant mortality (per 1000 births)\",\n", - " \"GDP ($ per capita)\"] as feature\n", - "MATCH (c:Country)\n", - "WHERE c[feature] IS null\n", - "MATCH (c)-[:PART_OF]->(r:Region)<-[:PART_OF]-(other:Country)\n", - "WHERE other[feature] IS NOT null\n", - "WITH c,feature,avg(other[feature]) as avg_value\n", - "CALL apoc.create.setProperty(c, feature, avg_value) \n", - "YIELD node\n", - "RETURN distinct 'missing values populated'\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(populate_missing_values)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Q9lmKAbQ25jL" - }, - "source": [ - "### Graph data science library\n", - "With Neo4j's Graph Data Science library, we can run more than 50 different graph algorithms directly in Neo4j. Algorithms are exposed as cypher procedures, similar to the APOC procedures we've seen above.\n", - "GDS uses a projection of the stored graph, that is entirely in-memory to achieve faster execution times." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "R4GNE06m25jK" - }, - "source": [ - "### Similarity network with cosine similarity\n", - "First, we much project the in-memory graph with GDS 2.0\n" + "text/plain": [ + " 'missing values populated'\n", + "0 missing values populated" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "populate_missing_values = \"\"\"\n", + "\n", + "UNWIND [\"Arable (%)\",\n", + " \"Crops (%)\",\n", + " \"Infant mortality (per 1000 births)\",\n", + " \"GDP ($ per capita)\"] as feature\n", + "MATCH (c:Country)\n", + "WHERE c[feature] IS null\n", + "MATCH (c)-[:PART_OF]->(r:Region)<-[:PART_OF]-(other:Country)\n", + "WHERE other[feature] IS NOT null\n", + "WITH c,feature,avg(other[feature]) as avg_value\n", + "CALL apoc.create.setProperty(c, feature, avg_value) \n", + "YIELD node\n", + "RETURN distinct 'missing values populated'\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(populate_missing_values)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q9lmKAbQ25jL" + }, + "source": [ + "### Graph data science library\n", + "With Neo4j's Graph Data Science library, we can run more than 50 different graph algorithms directly in Neo4j. Algorithms are exposed as cypher procedures, similar to the APOC procedures we've seen above.\n", + "GDS uses a projection of the stored graph, that is entirely in-memory to achieve faster execution times." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R4GNE06m25jK" + }, + "source": [ + "### Similarity network with cosine similarity\n", + "First, we much project the in-memory graph with GDS 2.0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "PvPiKn2y6LrG", + "outputId": "455d5d9f-2720-493e-bcd7-2b08ad7a803f" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "project_graph_query = \"\"\"\n", - "CALL gds.graph.project('countries', 'Country', '*', \n", - " {nodeProperties:['Arable (%)', 'Crops (%)', 'Infant mortality (per 1000 births)', 'GDP ($ per capita)',\n", - " 'Coastline (coast/area ratio)', 'Pop. Density (per sq. mi.)', 'Area (sq. mi.)']})\n", - "\"\"\"\n", - "\n", - "read_query(project_graph_query)" + "data": { + "text/html": [ + "
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" ], - "metadata": { - "id": "A3cTmugM6_v1", - "outputId": "36c8ea12-2d03-4e34-c5db-eb21d5f52deb", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "execution_count": 16, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodePropertiesWritten\n", - "0 227" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "cosine_similarity_query = \"\"\"\n", - "CALL gds.knn.mutate('countries', \n", - " {similarityCutoff:0.8, topK:10, nodeProperties: {countryFeatures: 'COSINE'},\n", - " mutateRelationshipType: 'SIMILAR', mutateProperty:'score'})\n", - "\"\"\"\n", - "\n", - "read_query(cosine_similarity_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aS3qw8QN25jM" - }, - "source": [ - "### Weakly connected components\n", - "More often than not, we start the graph analysis with the weakly connected components algorithm. It is a community detection algorithm used to find disconnected networks or islands within our graph. As we are only interested in the count of disconnected components, we can run the stats variant of the algorithm." + "text/plain": [ + " ranIterations nodePairsConsidered didConverge preProcessingMillis \\\n", + "0 5 78164 True 0 \n", + "\n", + " computeMillis mutateMillis postProcessingMillis nodesCompared \\\n", + "0 507 216 -1 227 \n", + "\n", + " relationshipsWritten similarityDistribution \\\n", + "0 2257 {'p1': 0.8574447631835938, 'max': 0.9999618530... \n", + "\n", + " configuration \n", + "0 {'topK': 10, 'maxIterations': 100, 'randomJoin... " ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cosine_similarity_query = \"\"\"\n", + "CALL gds.knn.mutate('countries', \n", + " {similarityCutoff:0.8, topK:10, nodeProperties: {countryFeatures: 'COSINE'},\n", + " mutateRelationshipType: 'SIMILAR', mutateProperty:'score'})\n", + "\"\"\"\n", + "\n", + "read_query(cosine_similarity_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aS3qw8QN25jM" + }, + "source": [ + "### Weakly connected components\n", + "More often than not, we start the graph analysis with the weakly connected components algorithm. It is a community detection algorithm used to find disconnected networks or islands within our graph. As we are only interested in the count of disconnected components, we can run the stats variant of the algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "fMNY-jFF25jM", + "outputId": "679e7ebf-bf3d-4e3f-a7c4-888fb3b5830c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "fMNY-jFF25jM", - "outputId": "679e7ebf-bf3d-4e3f-a7c4-888fb3b5830c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " componentCount min max mean p50 p75 p90\n", - "0 1 227 227 227.0 227 227 227" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "wcc_query = \"\"\"\n", - "\n", - "CALL gds.wcc.stats('countries', {relationshipTypes:['SIMILAR']})\n", - "YIELD componentCount, componentDistribution\n", - "RETURN componentCount, \n", - " componentDistribution.min as min,\n", - " componentDistribution.max as max,\n", - " componentDistribution.mean as mean,\n", - " componentDistribution.p50 as p50,\n", - " componentDistribution.p75 as p75,\n", - " componentDistribution.p90 as p90\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(wcc_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DVAmOn4D25jM" - }, - "source": [ - "The algorithm found only a single component within our graph. This is a favorable outcome as disconnected islands can skew the results of various other graph algorithms.\n", - "### Louvain algorithm\n", - "Another community detection algorithm is the Louvain algorithm. In basic terms, densely connected nodes are more likely to form a community. It relies on the modularity optimization to extract communities. The modularity optimization is performed in two steps. The first step involves optimizing the modularity locally. In the second step, it aggregates nodes belonging to the same community into a single node and builds a new network from those aggregated nodes. These two steps are repeated iteratively until a maximum of modularity is attained. A subtle side effect of these iterations is that we can take a look at the community structure at the end of each iteration, hence the Louvain algorithm is regarded as a hierarchical community detection algorithm. To include hierarchical community results, we must set the includeIntermediateCommunities parameter value to true." + "text/plain": [ + " componentCount min max mean p50 p75 p90\n", + "0 1 227 227 227.0 227 227 227" ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wcc_query = \"\"\"\n", + "\n", + "CALL gds.wcc.stats('countries', {relationshipTypes:['SIMILAR']})\n", + "YIELD componentCount, componentDistribution\n", + "RETURN componentCount, \n", + " componentDistribution.min as min,\n", + " componentDistribution.max as max,\n", + " componentDistribution.mean as mean,\n", + " componentDistribution.p50 as p50,\n", + " componentDistribution.p75 as p75,\n", + " componentDistribution.p90 as p90\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(wcc_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DVAmOn4D25jM" + }, + "source": [ + "The algorithm found only a single component within our graph. This is a favorable outcome as disconnected islands can skew the results of various other graph algorithms.\n", + "### Louvain algorithm\n", + "Another community detection algorithm is the Louvain algorithm. In basic terms, densely connected nodes are more likely to form a community. It relies on the modularity optimization to extract communities. The modularity optimization is performed in two steps. The first step involves optimizing the modularity locally. In the second step, it aggregates nodes belonging to the same community into a single node and builds a new network from those aggregated nodes. These two steps are repeated iteratively until a maximum of modularity is attained. A subtle side effect of these iterations is that we can take a look at the community structure at the end of each iteration, hence the Louvain algorithm is regarded as a hierarchical community detection algorithm. To include hierarchical community results, we must set the includeIntermediateCommunities parameter value to true." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "8tRYco1v25jN", + "outputId": "c7c471a4-aea0-4634-bef9-6a09f16d9d59" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "8tRYco1v25jN", - "outputId": "c7c471a4-aea0-4634-bef9-6a09f16d9d59", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ranLevels communityCount modularity \\\n", - "0 2 8 0.734819 \n", - "\n", - " modularities \n", - "0 [0.696204139379107, 0.7348188052372484] " - ], - "text/html": [ - "\n", - "
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0280.734601[0.6954974323961155, 0.734601100224988]
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" ], - "source": [ - "louvain_algo_query = \"\"\"\n", - "\n", - "CALL gds.louvain.write('countries', \n", - " {maxIterations:20,\n", - " relationshipTypes:['SIMILAR'],\n", - " includeIntermediateCommunities:true,\n", - " writeProperty:'louvain'})\n", - "YIELD ranLevels, communityCount,modularity,modularities\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(louvain_algo_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dAVvwSvr25jN" - }, - "source": [ - "We can observe by the ranLevels value that the Louvain algorithm found two levels of communities in our network. On the final level, it found eight g. We can now examine the extracted communities of the last level and compare their feature averages." + "text/plain": [ + " ranLevels communityCount modularity \\\n", + "0 2 8 0.734601 \n", + "\n", + " modularities \n", + "0 [0.6954974323961155, 0.734601100224988] " ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "louvain_algo_query = \"\"\"\n", + "\n", + "CALL gds.louvain.write('countries', \n", + " {maxIterations:20,\n", + " relationshipTypes:['SIMILAR'],\n", + " includeIntermediateCommunities:true,\n", + " writeProperty:'louvain'})\n", + "YIELD ranLevels, communityCount,modularity,modularities\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(louvain_algo_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dAVvwSvr25jN" + }, + "source": [ + "We can observe by the ranLevels value that the Louvain algorithm found two levels of communities in our network. On the final level, it found eight g. We can now examine the extracted communities of the last level and compare their feature averages." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 }, + "id": "Mb9vtbLa25jN", + "outputId": "7099c934-8b44-4c1c-f0eb-26ea6f919207" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "id": "Mb9vtbLa25jN", - "outputId": "7099c934-8b44-4c1c-f0eb-26ea6f919207", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 300 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " community community_size pct_arable pct_crops infant_mortality \\\n", - "0 12 44 5.464773 0.976591 8.682955 \n", - "1 54 25 18.504933 3.923393 8.290000 \n", - "2 43 23 4.273043 0.563913 29.380261 \n", - "3 50 41 31.129268 3.028049 20.186341 \n", - "4 57 26 13.234231 22.198077 23.057591 \n", - "5 30 10 22.116000 10.184000 55.267000 \n", - "6 21 24 14.853575 1.090783 69.105833 \n", - "7 23 34 3.931765 1.443529 91.808529 \n", - "\n", - " gdp coastline population_density area_size \\\n", - "0 22600.000000 42.745682 1283.050000 3.476954e+05 \n", - "1 18768.000000 11.308400 236.540000 2.930858e+05 \n", - "2 8393.913043 0.330000 23.886957 3.101448e+06 \n", - "3 7509.756098 15.717561 194.495122 2.044609e+05 \n", - "4 4465.384615 66.923462 370.726923 3.770538e+04 \n", - "5 2100.000000 2.702000 180.400000 2.983568e+05 \n", - "6 1870.833333 3.340000 100.829167 3.194362e+05 \n", - "7 1435.294118 4.170882 37.926471 6.419174e+05 \n", - "\n", - " example_members \n", - "0 [Andorra, Anguilla, Aruba] \n", - "1 [Argentina, Belgium, British Virgin Is.] \n", - "2 [Algeria, Australia, Belize] \n", - "3 [Albania, Antigua & Barbuda, Armenia] \n", - "4 [American Samoa, Cook Islands, Dominica] \n", - "5 [Burundi, Comoros, Ecuador] \n", - "6 [Azerbaijan, Benin, Burkina Faso] \n", - "7 [Afghanistan, Angola, Bhutan] " - ], - "text/html": [ - "\n", - "
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communitycommunity_sizepct_arablepct_cropsinfant_mortalitygdpcoastlinepopulation_densityarea_sizeexample_members
012445.4647730.9765918.68295522600.00000042.7456821283.0500003.476954e+05[Andorra, Anguilla, Aruba]
1542518.5049333.9233938.29000018768.00000011.308400236.5400002.930858e+05[Argentina, Belgium, British Virgin Is.]
243234.2730430.56391329.3802618393.9130430.33000023.8869573.101448e+06[Algeria, Australia, Belize]
3504131.1292683.02804920.1863417509.75609815.717561194.4951222.044609e+05[Albania, Antigua & Barbuda, Armenia]
4572613.23423122.19807723.0575914465.38461566.923462370.7269233.770538e+04[American Samoa, Cook Islands, Dominica]
5301022.11600010.18400055.2670002100.0000002.702000180.4000002.983568e+05[Burundi, Comoros, Ecuador]
6212414.8535751.09078369.1058331870.8333333.340000100.8291673.194362e+05[Azerbaijan, Benin, Burkina Faso]
723343.9317651.44352991.8085291435.2941184.17088237.9264716.419174e+05[Afghanistan, Angola, Bhutan]
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communitycommunity_sizepct_arablepct_cropsinfant_mortalitygdpcoastlinepopulation_densityarea_sizeexample_members
012465.5206521.4176098.67260922271.73913041.4463041235.8717393.337008e+05[Andorra, Anguilla, Aruba]
1542319.5271013.2976018.27652219091.30434811.173478239.8956523.163263e+05[Argentina, Belgium, British Virgin Is.]
243234.2730430.56391329.3802618393.9130430.33000023.8869573.101448e+06[Algeria, Australia, Belize]
3504131.1292683.02804920.1863417509.75609815.717561194.4951222.044609e+05[Albania, Antigua & Barbuda, Armenia]
4572613.23423122.19807723.0575914465.38461566.923462370.7269233.770538e+04[American Samoa, Cook Islands, Dominica]
5301022.11600010.18400055.2670002100.0000002.702000180.4000002.983568e+05[Burundi, Comoros, Ecuador]
6212414.8535751.09078369.1058331870.8333333.340000100.8291673.194362e+05[Azerbaijan, Benin, Burkina Faso]
723343.9317651.44352991.8085291435.2941184.17088237.9264716.419174e+05[Afghanistan, Angola, Bhutan]
\n", + "
" ], - "source": [ - "final_level_communities =\"\"\"\n", - "\n", - "MATCH (c:Country)\n", - "RETURN c.louvain[-1] as community,\n", - " count(*) as community_size,\n", - " avg(c['Arable (%)']) as pct_arable,\n", - " avg(c['Crops (%)']) as pct_crops, \n", - " avg(c['Infant mortality (per 1000 births)']) as infant_mortality,\n", - " avg(c['GDP ($ per capita)']) as gdp,\n", - " avg(c['Coastline (coast/area ratio)']) as coastline,\n", - " avg(c['Pop. Density (per sq. mi.)']) as population_density,\n", - " avg(c['Area (sq. mi.)']) as area_size,\n", - " collect(c['name'])[..3] as example_members\n", - "ORDER BY gdp DESC\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(final_level_communities)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ioqDNQ2r25jN" - }, - "source": [ - "Louvain algorithm found eight distinct communities within the similarity network. The biggest group has 51 countries as members and has the largest average GDP at almost 22 thousand dollars. They are second in infant mortality and the coastline ratio but lead in population density by a large margin. There are two communities with an average GDP of around 20 thousand dollars, and then we can observe a steep drop to 7000 dollars in third place. With the decline in GDP, we can also find the rise of infant mortality almost linearly. Another fascinating insight is that most of the more impoverished communities have little to no coastline.\n", - "### Find representatives of communities with PageRank\n", - "We can assess the top representatives of the final level communities with the PageRank algorithm. If we assume that each SIMILAR relationship is a vote of similarity between countries, the PageRank algorithm will assign the highest score to the most similar countries within the community. We will execute the PageRank algorithm for each community separately and consider only nodes and relationships within the given community. This can be easily achieved with cypher projection without any additional transformations." + "text/plain": [ + " community community_size pct_arable pct_crops infant_mortality \\\n", + "0 12 46 5.520652 1.417609 8.672609 \n", + "1 54 23 19.527101 3.297601 8.276522 \n", + "2 43 23 4.273043 0.563913 29.380261 \n", + "3 50 41 31.129268 3.028049 20.186341 \n", + "4 57 26 13.234231 22.198077 23.057591 \n", + "5 30 10 22.116000 10.184000 55.267000 \n", + "6 21 24 14.853575 1.090783 69.105833 \n", + "7 23 34 3.931765 1.443529 91.808529 \n", + "\n", + " gdp coastline population_density area_size \\\n", + "0 22271.739130 41.446304 1235.871739 3.337008e+05 \n", + "1 19091.304348 11.173478 239.895652 3.163263e+05 \n", + "2 8393.913043 0.330000 23.886957 3.101448e+06 \n", + "3 7509.756098 15.717561 194.495122 2.044609e+05 \n", + "4 4465.384615 66.923462 370.726923 3.770538e+04 \n", + "5 2100.000000 2.702000 180.400000 2.983568e+05 \n", + "6 1870.833333 3.340000 100.829167 3.194362e+05 \n", + "7 1435.294118 4.170882 37.926471 6.419174e+05 \n", + "\n", + " example_members \n", + "0 [Andorra, Anguilla, Aruba] \n", + "1 [Argentina, Belgium, British Virgin Is.] \n", + "2 [Algeria, Australia, Belize] \n", + "3 [Albania, Antigua & Barbuda, Armenia] \n", + "4 [American Samoa, Cook Islands, Dominica] \n", + "5 [Burundi, Comoros, Ecuador] \n", + "6 [Azerbaijan, Benin, Burkina Faso] \n", + "7 [Afghanistan, Angola, Bhutan] " ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_level_communities =\"\"\"\n", + "\n", + "MATCH (c:Country)\n", + "RETURN c.louvain[-1] as community,\n", + " count(*) as community_size,\n", + " avg(c['Arable (%)']) as pct_arable,\n", + " avg(c['Crops (%)']) as pct_crops, \n", + " avg(c['Infant mortality (per 1000 births)']) as infant_mortality,\n", + " avg(c['GDP ($ per capita)']) as gdp,\n", + " avg(c['Coastline (coast/area ratio)']) as coastline,\n", + " avg(c['Pop. Density (per sq. mi.)']) as population_density,\n", + " avg(c['Area (sq. mi.)']) as area_size,\n", + " collect(c['name'])[..3] as example_members\n", + "ORDER BY gdp DESC\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(final_level_communities)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ioqDNQ2r25jN" + }, + "source": [ + "Louvain algorithm found eight distinct communities within the similarity network. The biggest group has 51 countries as members and has the largest average GDP at almost 22 thousand dollars. They are second in infant mortality and the coastline ratio but lead in population density by a large margin. There are two communities with an average GDP of around 20 thousand dollars, and then we can observe a steep drop to 7000 dollars in third place. With the decline in GDP, we can also find the rise of infant mortality almost linearly. Another fascinating insight is that most of the more impoverished communities have little to no coastline.\n", + "### Find representatives of communities with PageRank\n", + "We can assess the top representatives of the final level communities with the PageRank algorithm. If we assume that each SIMILAR relationship is a vote of similarity between countries, the PageRank algorithm will assign the highest score to the most similar countries within the community. We will execute the PageRank algorithm for each community separately and consider only nodes and relationships within the given community. This can be easily achieved with cypher projection without any additional transformations." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 }, + "id": "s3jx3aLJ25jN", + "outputId": "157be139-2c30-4952-f92d-c018cb42617f" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "id": "s3jx3aLJ25jN", - "outputId": "157be139-2c30-4952-f92d-c018cb42617f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 300 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " community top_5_representatives\n", - "0 23 [Afghanistan, Angola, Bhutan, Bolivia, Central...\n", - "1 50 [Albania, Antigua & Barbuda, Armenia, Banglade...\n", - "2 43 [Algeria, Australia, Belize, Botswana, Brazil]\n", - "3 57 [American Samoa, Cook Islands, Dominica, Domin...\n", - "4 12 [Andorra, Anguilla, Aruba, Austria, Bahamas, The]\n", - "5 54 [Argentina, Belgium, British Virgin Is., Costa...\n", - "6 21 [Azerbaijan, Benin, Burkina Faso, Burma, Cambo...\n", - "7 30 [Burundi, Comoros, Ecuador, Ghana, Guatemala]" - ], - "text/html": [ - "\n", - "
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communitytop_5_representatives
023[Afghanistan, Angola, Bhutan, Bolivia, Central...
150[Albania, Antigua & Barbuda, Armenia, Banglade...
243[Algeria, Australia, Belize, Botswana, Brazil]
357[American Samoa, Cook Islands, Dominica, Domin...
412[Andorra, Anguilla, Aruba, Austria, Bahamas, The]
554[Argentina, Belgium, British Virgin Is., Costa...
621[Azerbaijan, Benin, Burkina Faso, Burma, Cambo...
730[Burundi, Comoros, Ecuador, Ghana, Guatemala]
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communitytop_5_representatives
023[Afghanistan, Angola, Bhutan, Bolivia, Central...
150[Albania, Antigua & Barbuda, Armenia, Banglade...
243[Algeria, Australia, Belize, Botswana, Brazil]
357[American Samoa, Cook Islands, Dominica, Domin...
412[Andorra, Anguilla, Aruba, Austria, Bahamas, The]
554[Argentina, Belgium, British Virgin Is., Eston...
621[Azerbaijan, Benin, Burkina Faso, Burma, Cambo...
730[Burundi, Comoros, Ecuador, Ghana, Guatemala]
\n", + "
" ], - "source": [ - "top_representatives_query = \"\"\"\n", - "\n", - "WITH 'MATCH (c:Country) WHERE c.louvain[-1] = $community \n", - " RETURN id(c) as id' as nodeQuery,\n", - " 'MATCH (s:Country)-[:SIMILAR]-(t:Country) \n", - " RETURN id(s) as source, id(t) as target' as relQuery\n", - "MATCH (c:Country)\n", - "WITH distinct c.louvain[-1] as community, nodeQuery, relQuery\n", - "CALL gds.graph.project.cypher(toString(community), nodeQuery, relQuery, {parameters:{community:community}})\n", - "YIELD nodeCount\n", - "CALL gds.pageRank.stream(toString(community))\n", - "YIELD nodeId, score\n", - "WITH community, nodeId,score\n", - "ORDER BY score DESC\n", - "RETURN community, \n", - " collect(gds.util.asNode(nodeId).name)[..5] as top_5_representatives\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(top_representatives_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8ybMwi_b25jO" - }, - "source": [ - "### Hierarchical communities based on the Louvain algorithm\n", - "We mentioned before that the Louvain algorithm can be used to find hierarchical communities with the includeIntermediateCommunities parameter and that in our example, it found two levels of communities. We will now examine the groups of countries on the first level. A rule of thumb is that communities on a lower level will be more granular and smaller." + "text/plain": [ + " community top_5_representatives\n", + "0 23 [Afghanistan, Angola, Bhutan, Bolivia, Central...\n", + "1 50 [Albania, Antigua & Barbuda, Armenia, Banglade...\n", + "2 43 [Algeria, Australia, Belize, Botswana, Brazil]\n", + "3 57 [American Samoa, Cook Islands, Dominica, Domin...\n", + "4 12 [Andorra, Anguilla, Aruba, Austria, Bahamas, The]\n", + "5 54 [Argentina, Belgium, British Virgin Is., Eston...\n", + "6 21 [Azerbaijan, Benin, Burkina Faso, Burma, Cambo...\n", + "7 30 [Burundi, Comoros, Ecuador, Ghana, Guatemala]" ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_representatives_query = \"\"\"\n", + "\n", + "WITH 'MATCH (c:Country) WHERE c.louvain[-1] = $community \n", + " RETURN id(c) as id' as nodeQuery,\n", + " 'MATCH (s:Country)-[:SIMILAR]-(t:Country) \n", + " RETURN id(s) as source, id(t) as target' as relQuery\n", + "MATCH (c:Country)\n", + "WITH distinct c.louvain[-1] as community, nodeQuery, relQuery\n", + "CALL gds.graph.project.cypher(toString(community), nodeQuery, relQuery, {parameters:{community:community}})\n", + "YIELD nodeCount\n", + "CALL gds.pageRank.stream(toString(community))\n", + "YIELD nodeId, score\n", + "WITH community, nodeId,score\n", + "ORDER BY score DESC\n", + "RETURN community, \n", + " collect(gds.util.asNode(nodeId).name)[..5] as top_5_representatives\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(top_representatives_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8ybMwi_b25jO" + }, + "source": [ + "### Hierarchical communities based on the Louvain algorithm\n", + "We mentioned before that the Louvain algorithm can be used to find hierarchical communities with the includeIntermediateCommunities parameter and that in our example, it found two levels of communities. We will now examine the groups of countries on the first level. A rule of thumb is that communities on a lower level will be more granular and smaller." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 520 }, + "id": "qJkNuPyq25jO", + "outputId": "6428afda-471f-4741-a4f7-5ce8a56e1da4" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "id": "qJkNuPyq25jO", - "outputId": "6428afda-471f-4741-a4f7-5ce8a56e1da4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 520 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " community community_size pct_arable pct_crops infant_mortality \\\n", - "0 12 18 11.282222 1.233889 5.994444 \n", - "1 54 14 20.745952 1.208201 6.735714 \n", - "2 38 26 1.437308 0.798462 10.544231 \n", - "3 7 11 15.652727 7.379091 10.268182 \n", - "4 50 12 26.865000 2.245000 8.755000 \n", - "5 43 16 5.606250 0.648750 25.319375 \n", - "6 30 12 38.811667 2.170000 13.029167 \n", - "7 39 7 1.225714 0.370000 38.662286 \n", - "8 57 15 17.153333 14.770000 22.656912 \n", - "9 56 11 7.890000 32.327273 23.603971 \n", - "10 9 17 28.716471 4.186471 33.307647 \n", - "11 44 10 22.116000 10.184000 55.267000 \n", - "12 21 16 17.386613 1.349300 66.530000 \n", - "13 0 8 9.787500 0.573750 74.257500 \n", - "14 23 34 3.931765 1.443529 91.808529 \n", - "\n", - " gdp coastline population_density area_size \\\n", - "0 27027.777778 15.930556 204.155556 6.800796e+05 \n", - "1 21335.714286 9.730714 296.542857 2.993842e+05 \n", - "2 19534.615385 61.310000 2029.976923 1.175833e+05 \n", - "3 15500.000000 13.316364 160.172727 2.850697e+05 \n", - "4 13275.000000 47.305000 268.258333 2.471858e+04 \n", - "5 9562.500000 0.282500 32.443750 4.310791e+06 \n", - "6 7425.000000 4.370000 130.316667 1.547768e+05 \n", - "7 5722.857143 0.438571 4.328571 3.372373e+05 \n", - "8 4800.000000 27.435333 537.733333 4.220207e+04 \n", - "9 4009.090909 120.770909 142.990909 3.157355e+04 \n", - "10 3500.000000 1.430588 187.729412 3.664089e+05 \n", - "11 2100.000000 2.702000 180.400000 2.983568e+05 \n", - "12 1881.250000 4.710625 121.756250 2.988618e+05 \n", - "13 1850.000000 0.598750 58.975000 3.605852e+05 \n", - "14 1435.294118 4.170882 37.926471 6.419174e+05 \n", - "\n", - " example_members \n", - "0 [Aruba, Austria, Bermuda] \n", - "1 [Argentina, Belgium, Estonia] \n", - "2 [Andorra, Anguilla, Bahamas, The] \n", - "3 [British Virgin Is., Costa Rica, Greece] \n", - "4 [Antigua & Barbuda, Barbados, Croatia] \n", - "5 [Algeria, Australia, Brazil] \n", - "6 [Belarus, Bulgaria, Cuba] \n", - "7 [Belize, Botswana, Gabon] \n", - "8 [American Samoa, Cook Islands, Dominican Repub... \n", - "9 [Dominica, Grenada, Kiribati] \n", - "10 [Albania, Armenia, Bangladesh] \n", - "11 [Burundi, Comoros, Ecuador] \n", - "12 [Azerbaijan, Benin, Burma] \n", - "13 [Burkina Faso, East Timor, Ethiopia] \n", - "14 [Afghanistan, Angola, Bhutan] " - ], - "text/html": [ - "\n", - "
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communitycommunity_sizepct_arablepct_cropsinfant_mortalitygdpcoastlinepopulation_densityarea_sizeexample_members
0121811.2822221.2338895.99444427027.77777815.930556204.1555566.800796e+05[Aruba, Austria, Bermuda]
1541420.7459521.2082016.73571421335.7142869.730714296.5428572.993842e+05[Argentina, Belgium, Estonia]
238261.4373080.79846210.54423119534.61538561.3100002029.9769231.175833e+05[Andorra, Anguilla, Bahamas, The]
371115.6527277.37909110.26818215500.00000013.316364160.1727272.850697e+05[British Virgin Is., Costa Rica, Greece]
4501226.8650002.2450008.75500013275.00000047.305000268.2583332.471858e+04[Antigua & Barbuda, Barbados, Croatia]
543165.6062500.64875025.3193759562.5000000.28250032.4437504.310791e+06[Algeria, Australia, Brazil]
6301238.8116672.17000013.0291677425.0000004.370000130.3166671.547768e+05[Belarus, Bulgaria, Cuba]
73971.2257140.37000038.6622865722.8571430.4385714.3285713.372373e+05[Belize, Botswana, Gabon]
8571517.15333314.77000022.6569124800.00000027.435333537.7333334.220207e+04[American Samoa, Cook Islands, Dominican Repub...
956117.89000032.32727323.6039714009.090909120.770909142.9909093.157355e+04[Dominica, Grenada, Kiribati]
1091728.7164714.18647133.3076473500.0000001.430588187.7294123.664089e+05[Albania, Armenia, Bangladesh]
11441022.11600010.18400055.2670002100.0000002.702000180.4000002.983568e+05[Burundi, Comoros, Ecuador]
12211617.3866131.34930066.5300001881.2500004.710625121.7562502.988618e+05[Azerbaijan, Benin, Burma]
13089.7875000.57375074.2575001850.0000000.59875058.9750003.605852e+05[Burkina Faso, East Timor, Ethiopia]
1423343.9317651.44352991.8085291435.2941184.17088237.9264716.419174e+05[Afghanistan, Angola, Bhutan]
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communitycommunity_sizepct_arablepct_cropsinfant_mortalitygdpcoastlinepopulation_densityarea_sizeexample_members
0122010.8290002.2225006.23950025830.00000015.623500203.5350006.146537e+05[Aruba, Austria, Bermuda]
1541420.7459521.2082016.73571421335.7142869.730714296.5428572.993842e+05[Argentina, Belgium, Estonia]
238261.4373080.79846210.54423119534.61538561.3100002029.9769231.175833e+05[Andorra, Anguilla, Bahamas, The]
37917.6311116.54777810.67333315600.00000013.417778151.7777783.426807e+05[British Virgin Is., Greece, Guadeloupe]
4501226.8650002.2450008.75500013275.00000047.305000268.2583332.471858e+04[Antigua & Barbuda, Barbados, Croatia]
543165.6062500.64875025.3193759562.5000000.28250032.4437504.310791e+06[Algeria, Australia, Brazil]
6301238.8116672.17000013.0291677425.0000004.370000130.3166671.547768e+05[Belarus, Bulgaria, Cuba]
73971.2257140.37000038.6622865722.8571430.4385714.3285713.372373e+05[Belize, Botswana, Gabon]
8571517.15333314.77000022.6569124800.00000027.435333537.7333334.220207e+04[American Samoa, Cook Islands, Dominican Repub...
956117.89000032.32727323.6039714009.090909120.770909142.9909093.157355e+04[Dominica, Grenada, Kiribati]
1091728.7164714.18647133.3076473500.0000001.430588187.7294123.664089e+05[Albania, Armenia, Bangladesh]
11441022.11600010.18400055.2670002100.0000002.702000180.4000002.983568e+05[Burundi, Comoros, Ecuador]
12211617.3866131.34930066.5300001881.2500004.710625121.7562502.988618e+05[Azerbaijan, Benin, Burma]
13089.7875000.57375074.2575001850.0000000.59875058.9750003.605852e+05[Burkina Faso, East Timor, Ethiopia]
1423343.9317651.44352991.8085291435.2941184.17088237.9264716.419174e+05[Afghanistan, Angola, Bhutan]
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" ], - "source": [ - "first_level_communities = \"\"\"\n", - "\n", - "MATCH (c:Country)\n", - "RETURN c.louvain[0] as community,\n", - " count(*) as community_size,\n", - " avg(c['Arable (%)']) as pct_arable,\n", - " avg(c['Crops (%)']) as pct_crops, \n", - " avg(c['Infant mortality (per 1000 births)']) as infant_mortality,\n", - " avg(c['GDP ($ per capita)']) as gdp,\n", - " avg(c['Coastline (coast/area ratio)']) as coastline,\n", - " avg(c['Pop. Density (per sq. mi.)']) as population_density,\n", - " avg(c['Area (sq. mi.)']) as area_size,\n", - " collect(c['name'])[..3] as example_members\n", - "ORDER BY gdp DESC\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(first_level_communities)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "S20aUzOJ25jO" - }, - "source": [ - "As expected, there are almost twice as many communities on the first level compared to the second and final level. An exciting community formed in second place by the average GDP. It contains only five countries, which are quite tiny as their average area size is only 364 square miles. On the other hand, they have a very high population density of around 10000 people per square mile. Example members are Macau, Monaco, and Hong Kong." + "text/plain": [ + " community community_size pct_arable pct_crops infant_mortality \\\n", + "0 12 20 10.829000 2.222500 6.239500 \n", + "1 54 14 20.745952 1.208201 6.735714 \n", + "2 38 26 1.437308 0.798462 10.544231 \n", + "3 7 9 17.631111 6.547778 10.673333 \n", + "4 50 12 26.865000 2.245000 8.755000 \n", + "5 43 16 5.606250 0.648750 25.319375 \n", + "6 30 12 38.811667 2.170000 13.029167 \n", + "7 39 7 1.225714 0.370000 38.662286 \n", + "8 57 15 17.153333 14.770000 22.656912 \n", + "9 56 11 7.890000 32.327273 23.603971 \n", + "10 9 17 28.716471 4.186471 33.307647 \n", + "11 44 10 22.116000 10.184000 55.267000 \n", + "12 21 16 17.386613 1.349300 66.530000 \n", + "13 0 8 9.787500 0.573750 74.257500 \n", + "14 23 34 3.931765 1.443529 91.808529 \n", + "\n", + " gdp coastline population_density area_size \\\n", + "0 25830.000000 15.623500 203.535000 6.146537e+05 \n", + "1 21335.714286 9.730714 296.542857 2.993842e+05 \n", + "2 19534.615385 61.310000 2029.976923 1.175833e+05 \n", + "3 15600.000000 13.417778 151.777778 3.426807e+05 \n", + "4 13275.000000 47.305000 268.258333 2.471858e+04 \n", + "5 9562.500000 0.282500 32.443750 4.310791e+06 \n", + "6 7425.000000 4.370000 130.316667 1.547768e+05 \n", + "7 5722.857143 0.438571 4.328571 3.372373e+05 \n", + "8 4800.000000 27.435333 537.733333 4.220207e+04 \n", + "9 4009.090909 120.770909 142.990909 3.157355e+04 \n", + "10 3500.000000 1.430588 187.729412 3.664089e+05 \n", + "11 2100.000000 2.702000 180.400000 2.983568e+05 \n", + "12 1881.250000 4.710625 121.756250 2.988618e+05 \n", + "13 1850.000000 0.598750 58.975000 3.605852e+05 \n", + "14 1435.294118 4.170882 37.926471 6.419174e+05 \n", + "\n", + " example_members \n", + "0 [Aruba, Austria, Bermuda] \n", + "1 [Argentina, Belgium, Estonia] \n", + "2 [Andorra, Anguilla, Bahamas, The] \n", + "3 [British Virgin Is., Greece, Guadeloupe] \n", + "4 [Antigua & Barbuda, Barbados, Croatia] \n", + "5 [Algeria, Australia, Brazil] \n", + "6 [Belarus, Bulgaria, Cuba] \n", + "7 [Belize, Botswana, Gabon] \n", + "8 [American Samoa, Cook Islands, Dominican Repub... \n", + "9 [Dominica, Grenada, Kiribati] \n", + "10 [Albania, Armenia, Bangladesh] \n", + "11 [Burundi, Comoros, Ecuador] \n", + "12 [Azerbaijan, Benin, Burma] \n", + "13 [Burkina Faso, East Timor, Ethiopia] \n", + "14 [Afghanistan, Angola, Bhutan] " ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_level_communities = \"\"\"\n", + "\n", + "MATCH (c:Country)\n", + "RETURN c.louvain[0] as community,\n", + " count(*) as community_size,\n", + " avg(c['Arable (%)']) as pct_arable,\n", + " avg(c['Crops (%)']) as pct_crops, \n", + " avg(c['Infant mortality (per 1000 births)']) as infant_mortality,\n", + " avg(c['GDP ($ per capita)']) as gdp,\n", + " avg(c['Coastline (coast/area ratio)']) as coastline,\n", + " avg(c['Pop. Density (per sq. mi.)']) as population_density,\n", + " avg(c['Area (sq. mi.)']) as area_size,\n", + " collect(c['name'])[..3] as example_members\n", + "ORDER BY gdp DESC\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(first_level_communities)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S20aUzOJ25jO" + }, + "source": [ + "As expected, there are almost twice as many communities on the first level compared to the second and final level. An exciting community formed in second place by the average GDP. It contains only five countries, which are quite tiny as their average area size is only 364 square miles. On the other hand, they have a very high population density of around 10000 people per square mile. Example members are Macau, Monaco, and Hong Kong." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 }, + "id": "aI450Ksi25jO", + "outputId": "94299495-50cf-47a4-9ccb-885c3c687061" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "id": "aI450Ksi25jO", - "outputId": "94299495-50cf-47a4-9ccb-885c3c687061", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 300 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " 'dropped graph: ' + graphName\n", - "0 dropped graph: 30\n", - "1 dropped graph: 50\n", - "2 dropped graph: 23\n", - "3 dropped graph: 21\n", - "4 dropped graph: 43\n", - "5 dropped graph: 54\n", - "6 dropped graph: 57\n", - "7 dropped graph: 12" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "drop_all_graphs = \"\"\"\n", - "CALL gds.graph.list() YIELD graphName\n", - "CALL gds.graph.drop(graphName) YIELD graphName as t\n", - "RETURN 'dropped graph: ' + graphName AS result\n", - "\"\"\"\n", - "\n", - "read_query(drop_all_graphs)" + "text/plain": [ + " result\n", + "0 dropped graph: 57\n", + "1 dropped graph: 23\n", + "2 dropped graph: 12\n", + "3 dropped graph: 43\n", + "4 dropped graph: 54\n", + "5 dropped graph: 21\n", + "6 dropped graph: 30\n", + "7 dropped graph: 50\n", + "8 dropped graph: countries" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "rNRoILif25jP" - }, - "outputs": [], - "source": [ - "" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - }, - "colab": { - "name": "Countries of the world analysis.ipynb", - "provenance": [], - "include_colab_link": true + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "drop_all_graphs = \"\"\"\n", + "CALL gds.graph.list() YIELD graphName\n", + "CALL gds.graph.drop(graphName) YIELD graphName as t\n", + "RETURN 'dropped graph: ' + graphName AS result\n", + "\"\"\"\n", + "\n", + "read_query(drop_all_graphs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rNRoILif25jP" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "include_colab_link": true, + "name": "Countries of the world analysis.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/GDS_Multigraph/GDS multigraph.ipynb b/GDS_Multigraph/GDS multigraph.ipynb index ec33776..9bde7a6 100644 --- a/GDS_Multigraph/GDS multigraph.ipynb +++ b/GDS_Multigraph/GDS multigraph.ipynb @@ -1,3335 +1,3342 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Countries of the world\n", - "* Updated to GDS 2.0 version\n", - "* Link to original blog post: https://towardsdatascience.com/analyzing-multigraphs-in-neo4j-graph-data-science-library-35c9b6d20099" - ], - "metadata": { - "id": "LLgDODQj__no" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install neo4j" - ], - "metadata": { - "id": "IFUfNyE-AVsB", - "outputId": "819a09b9-f95f-4564-c9d2-c15b2180adfe", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting neo4j\n", - " Downloading neo4j-4.4.2.tar.gz (89 kB)\n", - "\u001b[?25l\r\u001b[K |███▋ | 10 kB 19.7 MB/s eta 0:00:01\r\u001b[K |███████▎ | 20 kB 11.7 MB/s eta 0:00:01\r\u001b[K |███████████ | 30 kB 9.4 MB/s eta 0:00:01\r\u001b[K |██████████████▋ | 40 kB 8.6 MB/s eta 0:00:01\r\u001b[K |██████████████████▎ | 51 kB 4.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████ | 61 kB 5.3 MB/s eta 0:00:01\r\u001b[K |█████████████████████████▋ | 71 kB 5.4 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▎ | 81 kB 6.0 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 89 kB 3.6 MB/s \n", - "\u001b[?25hRequirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j) (2018.9)\n", - "Building wheels for collected packages: neo4j\n", - " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for neo4j: filename=neo4j-4.4.2-py3-none-any.whl size=115365 sha256=6c51df58ee1c80464d849e1d3c9a4220ade9ca8512ba25124abaa9900d8f2143\n", - " Stored in directory: /root/.cache/pip/wheels/10/d6/28/95029d7f69690dbc3b93e4933197357987de34fbd44b50a0e4\n", - "Successfully built neo4j\n", - "Installing collected packages: neo4j\n", - "Successfully installed neo4j-4.4.2\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "I recommend you setup a [blank project on Neo4j Sandbox environment](https://sandbox.neo4j.com/?usecase=blank-sandbox), but you can also use other environment versions" - ], - "metadata": { - "id": "MkzxLkbTAZ2V" - } - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "Alhkhjqf_9sQ" - }, - "outputs": [], - "source": [ - "# Define Neo4j connections\n", - "from neo4j import GraphDatabase\n", - "host = 'bolt://3.235.2.228:7687'\n", - "user = 'neo4j'\n", - "password = 'seats-drunks-carbon'\n", - "driver = GraphDatabase.driver(host,auth=(user, password))\n", - "\n", - "def drop_graph(name):\n", - " with driver.session() as session:\n", - " drop_graph_query = \"\"\"\n", - " CALL gds.graph.drop('{}');\n", - " \"\"\".format(name)\n", - " session.run(drop_graph_query)" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LLgDODQj__no" + }, + "source": [ + "# Countries of the world\n", + "* Updated to GDS 2.0 version\n", + "* Link to original blog post: https://towardsdatascience.com/analyzing-multigraphs-in-neo4j-graph-data-science-library-35c9b6d20099" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "IFUfNyE-AVsB", + "outputId": "819a09b9-f95f-4564-c9d2-c15b2180adfe" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "yvbJf7zF_9sU" - }, - "outputs": [], - "source": [ - "# Import libraries\n", - "import pandas as pd\n", - "\n", - "def read_query(query):\n", - " with driver.session() as session:\n", - " result = session.run(query)\n", - " return pd.DataFrame([r.values() for r in result], columns=result.keys())" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting neo4j\n", + " Downloading neo4j-4.4.2.tar.gz (89 kB)\n", + "\u001b[?25l\r", + "\u001b[K |███▋ | 10 kB 19.7 MB/s eta 0:00:01\r", + "\u001b[K |███████▎ | 20 kB 11.7 MB/s eta 0:00:01\r", + "\u001b[K |███████████ | 30 kB 9.4 MB/s eta 0:00:01\r", + "\u001b[K |██████████████▋ | 40 kB 8.6 MB/s eta 0:00:01\r", + "\u001b[K |██████████████████▎ | 51 kB 4.5 MB/s eta 0:00:01\r", + "\u001b[K |██████████████████████ | 61 kB 5.3 MB/s eta 0:00:01\r", + "\u001b[K |█████████████████████████▋ | 71 kB 5.4 MB/s eta 0:00:01\r", + "\u001b[K |█████████████████████████████▎ | 81 kB 6.0 MB/s eta 0:00:01\r", + "\u001b[K |████████████████████████████████| 89 kB 3.6 MB/s \n", + "\u001b[?25hRequirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j) (2018.9)\n", + "Building wheels for collected packages: neo4j\n", + " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for neo4j: filename=neo4j-4.4.2-py3-none-any.whl size=115365 sha256=6c51df58ee1c80464d849e1d3c9a4220ade9ca8512ba25124abaa9900d8f2143\n", + " Stored in directory: /root/.cache/pip/wheels/10/d6/28/95029d7f69690dbc3b93e4933197357987de34fbd44b50a0e4\n", + "Successfully built neo4j\n", + "Installing collected packages: neo4j\n", + "Successfully installed neo4j-4.4.2\n" + ] + } + ], + "source": [ + "!pip install neo4j" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MkzxLkbTAZ2V" + }, + "source": [ + "I recommend you setup a [blank project on Neo4j Sandbox environment](https://sandbox.neo4j.com/?usecase=blank-sandbox), but you can also use other environment versions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "Alhkhjqf_9sQ" + }, + "outputs": [], + "source": [ + "# Define Neo4j connections\n", + "from neo4j import GraphDatabase\n", + "host = 'bolt://3.235.2.228:7687'\n", + "user = 'neo4j'\n", + "password = 'seats-drunks-carbon'\n", + "driver = GraphDatabase.driver(host,auth=(user, password))\n", + "\n", + "def drop_graph(name):\n", + " with driver.session() as session:\n", + " drop_graph_query = \"\"\"\n", + " CALL gds.graph.drop('{}');\n", + " \"\"\".format(name)\n", + " session.run(drop_graph_query)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "yvbJf7zF_9sU" + }, + "outputs": [], + "source": [ + "# Import libraries\n", + "import pandas as pd\n", + "\n", + "def read_query(query):\n", + " with driver.session() as session:\n", + " result = session.run(query)\n", + " return pd.DataFrame([r.values() for r in result], columns=result.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49 }, + "id": "mhNpPk_D_9sU", + "outputId": "c95e1870-7e50-4fb0-d046-16b7df80761f" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "mhNpPk_D_9sU", - "outputId": "c95e1870-7e50-4fb0-d046-16b7df80761f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: []" - ], - "text/html": [ - "\n", - "
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\n", + " " ], - "source": [ - "# Import the graph\n", - "\n", - "import_query = \"\"\"\n", - "CREATE (t:Entity{name:'Tomaz'}),\n", - " (n:Entity{name:'Neo4j'})\n", - "CREATE (t)-[:LIKES{weight:1}]->(n),\n", - " (t)-[:LOVES{weight:2}]->(n),\n", - " (t)-[:PRESENTED_FOR{weight:0.5}]->(n),\n", - " (t)-[:PRESENTED_FOR{weight:1.5}]->(n);\n", - "\"\"\"\n", - "read_query(import_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M9KtdcIh_9sV" - }, - "source": [ - "## Relationships without own identity\n", - "\n", - "In the context of the GDS library, relationships without own identity imply that we ignore the type of relationships in the process of projecting the graph.\n", - "\n", - "### Native projection\n", - "\n", - "We will start with native projection examples. If we use the wildcard operator * to define the relationships we want to project, we ignore their type and bundle them all together. This can be understood as losing their own identity (type in the context of Neo4j).\n", - "\n", - "#### Default aggregation strategy\n", - "\n", - "In the first example, we will observe the default behavior of the graph projection process." + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import the graph\n", + "\n", + "import_query = \"\"\"\n", + "CREATE (t:Entity{name:'Tomaz'}),\n", + " (n:Entity{name:'Neo4j'})\n", + "CREATE (t)-[:LIKES{weight:1}]->(n),\n", + " (t)-[:LOVES{weight:2}]->(n),\n", + " (t)-[:PRESENTED_FOR{weight:0.5}]->(n),\n", + " (t)-[:PRESENTED_FOR{weight:1.5}]->(n);\n", + "\"\"\"\n", + "read_query(import_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M9KtdcIh_9sV" + }, + "source": [ + "## Relationships without own identity\n", + "\n", + "In the context of the GDS library, relationships without own identity imply that we ignore the type of relationships in the process of projecting the graph.\n", + "\n", + "### Native projection\n", + "\n", + "We will start with native projection examples. If we use the wildcard operator * to define the relationships we want to project, we ignore their type and bundle them all together. This can be understood as losing their own identity (type in the context of Neo4j).\n", + "\n", + "#### Default aggregation strategy\n", + "\n", + "In the first example, we will observe the default behavior of the graph projection process." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "lj3scEOR_9sX", + "outputId": "8f3978fe-380c-4181-a475-7fd2d0d4f0ff" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "lj3scEOR_9sX", - "outputId": "8f3978fe-380c-4181-a475-7fd2d0d4f0ff", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeProjection \\\n", - "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", - "\n", - " relationshipProjection graphName nodeCount \\\n", - "0 {'__ALL__': {'orientation': 'NATURAL', 'aggreg... default_agg 2 \n", - "\n", - " relationshipCount projectMillis \n", - "0 4 80 " - ], - "text/html": [ - "\n", - "
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\n", + " " ], - "source": [ - "default_agg_strategy = \"\"\"\n", - "\n", - "CALL gds.graph.project('default_agg','*','*',\n", - " {relationshipProperties: ['weight']})\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(default_agg_strategy)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FwHVohfA_9sY" - }, - "source": [ - "The default aggregation strategy actually doesn't perform any aggregations and projects all the relationships from the stored graph to memory without any transformations. If we check the relationshipCount, we observe that four relationships have been projected. To double-check the projected graph, we can use the degree centrality." + "text/plain": [ + " nodeProjection \\\n", + "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", + "\n", + " relationshipProjection graphName nodeCount \\\n", + "0 {'__ALL__': {'orientation': 'NATURAL', 'aggreg... default_agg 2 \n", + "\n", + " relationshipCount projectMillis \n", + "0 4 80 " ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "default_agg_strategy = \"\"\"\n", + "\n", + "CALL gds.graph.project('default_agg','*','*',\n", + " {relationshipProperties: ['weight']})\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(default_agg_strategy)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FwHVohfA_9sY" + }, + "source": [ + "The default aggregation strategy actually doesn't perform any aggregations and projects all the relationships from the stored graph to memory without any transformations. If we check the relationshipCount, we observe that four relationships have been projected. To double-check the projected graph, we can use the degree centrality." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 }, + "id": "Kbj1-VQb_9sZ", + "outputId": "7ed9bc0d-54b5-4a63-d1dd-66ab731e2fee" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "Kbj1-VQb_9sZ", - "outputId": "7ed9bc0d-54b5-4a63-d1dd-66ab731e2fee", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name degree\n", - "0 Tomaz 4.0\n", - "1 Neo4j 0.0" - ], - "text/html": [ - "\n", - "
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\n", + " " ], - "source": [ - "default_agg_strategy_check = \"\"\"\n", - "\n", - "CALL gds.degree.stream('default_agg')\n", - "YIELD nodeId, score\n", - "RETURN gds.util.asNode(nodeId).name AS name, \n", - " score AS degree\n", - "ORDER BY degree DESC\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(default_agg_strategy_check)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hhEtoaIP_9sZ" - }, - "source": [ - "As we expected, all four relationships have been projected. To have a reference for the future let's also calculate the weighted degree centrality. By adding the relationshipWeightProperty parameter, we indicate we want to use the weighted variant of the algorithm." + "text/plain": [ + " name degree\n", + "0 Tomaz 4.0\n", + "1 Neo4j 0.0" ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "default_agg_strategy_check = \"\"\"\n", + "\n", + "CALL gds.degree.stream('default_agg')\n", + "YIELD nodeId, score\n", + "RETURN gds.util.asNode(nodeId).name AS name, \n", + " score AS degree\n", + "ORDER BY degree DESC\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(default_agg_strategy_check)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hhEtoaIP_9sZ" + }, + "source": [ + "As we expected, all four relationships have been projected. To have a reference for the future let's also calculate the weighted degree centrality. By adding the relationshipWeightProperty parameter, we indicate we want to use the weighted variant of the algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 }, + "id": "P68OfMz6_9sa", + "outputId": "bc0bce25-4a2e-4405-a271-6b923d1941b4" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "P68OfMz6_9sa", - "outputId": "bc0bce25-4a2e-4405-a271-6b923d1941b4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name weighted_degree\n", - "0 Tomaz 5.0\n", - "1 Neo4j 0.0" - ], - "text/html": [ - "\n", - "
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nameweighted_degree
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nameweighted_degree
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\n", + " " ], - "source": [ - "default_agg_strategy_weight_check = \"\"\"\n", - "\n", - "CALL gds.degree.stream('default_agg', \n", - " {relationshipWeightProperty:'weight'})\n", - "YIELD nodeId, score \n", - "RETURN gds.util.asNode(nodeId).name AS name,\n", - " score AS weighted_degree ORDER BY weighted_degree DESC\n", - "\"\"\"\n", - "\n", - "read_query(default_agg_strategy_weight_check)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ExWNKGBd_9sb" - }, - "source": [ - "The result is the sum of weights of all the considered relationships. We have no use of this projected graph anymore, so remember to release it from memory." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "W4mS6CEy_9sb" - }, - "outputs": [], - "source": [ - "drop_graph('default_agg')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "g7hJLJFb_9sb" - }, - "source": [ - "#### Single-graph strategy\n", - "\n", - "Depending on the use case, we might want to reduce our multigraph to a single graph during the projection process. This can be easily achieved with the aggregation parameter. We have to use the configuration map variant for the relationship definition." + "text/plain": [ + " name weighted_degree\n", + "0 Tomaz 5.0\n", + "1 Neo4j 0.0" ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "default_agg_strategy_weight_check = \"\"\"\n", + "\n", + "CALL gds.degree.stream('default_agg', \n", + " {relationshipWeightProperty:'weight'})\n", + "YIELD nodeId, score \n", + "RETURN gds.util.asNode(nodeId).name AS name,\n", + " score AS weighted_degree ORDER BY weighted_degree DESC\n", + "\"\"\"\n", + "\n", + "read_query(default_agg_strategy_weight_check)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ExWNKGBd_9sb" + }, + "source": [ + "The result is the sum of weights of all the considered relationships. We have no use of this projected graph anymore, so remember to release it from memory." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "W4mS6CEy_9sb" + }, + "outputs": [], + "source": [ + "drop_graph('default_agg')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g7hJLJFb_9sb" + }, + "source": [ + "#### Single-graph strategy\n", + "\n", + "Depending on the use case, we might want to reduce our multigraph to a single graph during the projection process. This can be easily achieved with the aggregation parameter. We have to use the configuration map variant for the relationship definition." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "NmUnMJLG_9sc", + "outputId": "535d53c3-fe5f-4765-f5cd-1526119a7c1d" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "NmUnMJLG_9sc", - "outputId": "535d53c3-fe5f-4765-f5cd-1526119a7c1d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeProjection \\\n", - "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", - "\n", - " relationshipProjection graphName \\\n", - "0 {'TYPE': {'orientation': 'NATURAL', 'aggregati... single_rel_strategy \n", - "\n", - " nodeCount relationshipCount projectMillis \n", - "0 2 1 93 " - ], - "text/html": [ - "\n", - "
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nodeProjectionrelationshipProjectiongraphNamenodeCountrelationshipCountprojectMillis
0{'__ALL__': {'label': '*', 'properties': {}}}{'TYPE': {'orientation': 'NATURAL', 'aggregati...single_rel_strategy2193
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nodeProjectionrelationshipProjectiongraphNamenodeCountrelationshipCountprojectMillis
0{'__ALL__': {'label': '*', 'properties': {}}}{'TYPE': {'orientation': 'NATURAL', 'aggregati...single_rel_strategy2193
\n", + "
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\n", + " " ], - "source": [ - "single_rel_graph = \"\"\"\n", - "CALL gds.graph.project('single_rel_strategy','*', \n", - " {TYPE:{type:'*', aggregation:'SINGLE'}})\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(single_rel_graph)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zKpEMOIu_9sc" - }, - "source": [ - "We notice by looking at the relationshipCount, that only a single relationship has been projected. If we want to double-check with the degree centrality:" + "text/plain": [ + " nodeProjection \\\n", + "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", + "\n", + " relationshipProjection graphName \\\n", + "0 {'TYPE': {'orientation': 'NATURAL', 'aggregati... single_rel_strategy \n", + "\n", + " nodeCount relationshipCount projectMillis \n", + "0 2 1 93 " ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "single_rel_graph = \"\"\"\n", + "CALL gds.graph.project('single_rel_strategy','*', \n", + " {TYPE:{type:'*', aggregation:'SINGLE'}})\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(single_rel_graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zKpEMOIu_9sc" + }, + "source": [ + "We notice by looking at the relationshipCount, that only a single relationship has been projected. If we want to double-check with the degree centrality:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 }, + "id": "ltHqaNHA_9sd", + "outputId": "6f88b61f-3fad-4107-c1c9-1aa0d3ddb495" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "ltHqaNHA_9sd", - "outputId": "6f88b61f-3fad-4107-c1c9-1aa0d3ddb495", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name degree\n", - "0 Tomaz 1.0\n", - "1 Neo4j 0.0" - ], - "text/html": [ - "\n", - "
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namedegree
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namedegree
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\n", + " " ], - "source": [ - "single_rel_graph_check = \"\"\"\n", - "\n", - "CALL gds.degree.stream('single_rel_strategy')\n", - "YIELD nodeId, score\n", - "RETURN gds.util.asNode(nodeId).name AS name,\n", - " score AS degree\n", - "ORDER BY degree DESC\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(single_rel_graph_check)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "vhwpHLbC_9sd" - }, - "outputs": [], - "source": [ - "drop_graph('single_rel_strategy')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "akCjEafL_9sd" - }, - "source": [ - "#### Property aggregation strategies\n", - "\n", - "We have looked at the unweighted multigraph so far. Now it is time to look at what happens when we are dealing with a weighted multigraph and we want to reduce it to a single graph. There are three different strategies we can pick for property aggregations:\n", - "\n", - "* MIN: minimum value of all weights is projected\n", - "* MAX: maximum value of all weights is projected\n", - "* SUM: the sum of all weights is projected\n", - "\n", - "In our next example, we will use the MIN property aggregation strategy to reduce a weighted multigraph to a single graph. By providing the property aggregation parameter, we indicate we want to reduce the stored graph to a single graph in the projection process." + "text/plain": [ + " name degree\n", + "0 Tomaz 1.0\n", + "1 Neo4j 0.0" ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "single_rel_graph_check = \"\"\"\n", + "\n", + "CALL gds.degree.stream('single_rel_strategy')\n", + "YIELD nodeId, score\n", + "RETURN gds.util.asNode(nodeId).name AS name,\n", + " score AS degree\n", + "ORDER BY degree DESC\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(single_rel_graph_check)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "vhwpHLbC_9sd" + }, + "outputs": [], + "source": [ + "drop_graph('single_rel_strategy')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "akCjEafL_9sd" + }, + "source": [ + "#### Property aggregation strategies\n", + "\n", + "We have looked at the unweighted multigraph so far. Now it is time to look at what happens when we are dealing with a weighted multigraph and we want to reduce it to a single graph. There are three different strategies we can pick for property aggregations:\n", + "\n", + "* MIN: minimum value of all weights is projected\n", + "* MAX: maximum value of all weights is projected\n", + "* SUM: the sum of all weights is projected\n", + "\n", + "In our next example, we will use the MIN property aggregation strategy to reduce a weighted multigraph to a single graph. By providing the property aggregation parameter, we indicate we want to reduce the stored graph to a single graph in the projection process." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "I_u1X2ar_9sd", + "outputId": "962da3f6-68d8-41b5-91f9-52d7d6c2895c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "I_u1X2ar_9sd", - "outputId": "962da3f6-68d8-41b5-91f9-52d7d6c2895c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeProjection \\\n", - "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", - "\n", - " relationshipProjection graphName \\\n", - "0 {'__ALL__': {'orientation': 'NATURAL', 'aggreg... min_aggregation \n", - "\n", - " nodeCount relationshipCount projectMillis \n", - "0 2 1 16 " - ], - "text/html": [ - "\n", - "
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nodeProjectionrelationshipProjectiongraphNamenodeCountrelationshipCountprojectMillis
0{'__ALL__': {'label': '*', 'properties': {}}}{'__ALL__': {'orientation': 'NATURAL', 'aggreg...min_aggregation2116
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
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\n", + " " ], - "source": [ - "min_agg_strategy = \"\"\"\n", - "\n", - "CALL gds.graph.project('min_aggregation','*','*',\n", - " {relationshipProperties: {weight: {property: 'weight', aggregation: 'MIN'}}})\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(min_agg_strategy)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oqbgaP9D_9se" - }, - "source": [ - "We can observe that the relationshipCount is 1, which means our multigraph has been successfully reduced to a single graph. To validate the MIN property aggregation, let's also calculate the weighted degree centrality." + "text/plain": [ + " nodeProjection \\\n", + "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", + "\n", + " relationshipProjection graphName \\\n", + "0 {'__ALL__': {'orientation': 'NATURAL', 'aggreg... min_aggregation \n", + "\n", + " nodeCount relationshipCount projectMillis \n", + "0 2 1 16 " ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_agg_strategy = \"\"\"\n", + "\n", + "CALL gds.graph.project('min_aggregation','*','*',\n", + " {relationshipProperties: {weight: {property: 'weight', aggregation: 'MIN'}}})\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(min_agg_strategy)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oqbgaP9D_9se" + }, + "source": [ + "We can observe that the relationshipCount is 1, which means our multigraph has been successfully reduced to a single graph. To validate the MIN property aggregation, let's also calculate the weighted degree centrality." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 }, + "id": "WbePfg2Y_9se", + "outputId": "9285a320-9c2d-4dd9-849d-53e097f3d856" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "WbePfg2Y_9se", - "outputId": "9285a320-9c2d-4dd9-849d-53e097f3d856", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name weighted_degree\n", - "0 Tomaz 0.5\n", - "1 Neo4j 0.0" - ], - "text/html": [ - "\n", - "
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\n", + " " ], - "source": [ - "min_agg_strategy_check = \"\"\"\n", - "\n", - "CALL gds.degree.stream('min_aggregation', \n", - " {relationshipWeightProperty:'weight'})\n", - "YIELD nodeId, score\n", - "RETURN gds.util.asNode(nodeId).name AS name, \n", - " score AS weighted_degree\n", - "ORDER BY weighted_degree DESC\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(min_agg_strategy_check)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Qk5MNNGf_9se" - }, - "source": [ - "As we expected with the MIN property aggregation strategy, the reduced single weight was the smallest one. Again, as we finished with the example,  don't forget to drop the projected graph." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "_gP6qZnd_9sf" - }, - "outputs": [], - "source": [ - "drop_graph('min_aggregation')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7lFgjXVq_9sf" - }, - "source": [ - "### Cypher projection\n", - "\n", - "Let's recreate the above examples with cypher projection. To lose the identity of the relationships and bundle them all together, we avoid providing the type column in the return of the relationship statement.\n", - "\n", - "#### Default aggregation strategy\n", - "\n", - "Similarly to native projection, the default setting in cypher projection is to project all the relationships without any transformation during the projection process." + "text/plain": [ + " name weighted_degree\n", + "0 Tomaz 0.5\n", + "1 Neo4j 0.0" ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min_agg_strategy_check = \"\"\"\n", + "\n", + "CALL gds.degree.stream('min_aggregation', \n", + " {relationshipWeightProperty:'weight'})\n", + "YIELD nodeId, score\n", + "RETURN gds.util.asNode(nodeId).name AS name, \n", + " score AS weighted_degree\n", + "ORDER BY weighted_degree DESC\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(min_agg_strategy_check)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Qk5MNNGf_9se" + }, + "source": [ + "As we expected with the MIN property aggregation strategy, the reduced single weight was the smallest one. Again, as we finished with the example,  don't forget to drop the projected graph." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "_gP6qZnd_9sf" + }, + "outputs": [], + "source": [ + "drop_graph('min_aggregation')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7lFgjXVq_9sf" + }, + "source": [ + "### Cypher projection\n", + "\n", + "Let's recreate the above examples with cypher projection. To lose the identity of the relationships and bundle them all together, we avoid providing the type column in the return of the relationship statement.\n", + "\n", + "#### Default aggregation strategy\n", + "\n", + "Similarly to native projection, the default setting in cypher projection is to project all the relationships without any transformation during the projection process." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "hz0qkwTb_9sf", + "outputId": "78242376-a9ba-46b3-fd83-61c6e9de5339" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "hz0qkwTb_9sf", - "outputId": "78242376-a9ba-46b3-fd83-61c6e9de5339", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeQuery \\\n", - "0 MATCH (n:Entity) RETURN id(n) AS id \n", - "\n", - " relationshipQuery graphName \\\n", - "0 MATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ... cypher_default_strategy \n", - "\n", - " nodeCount relationshipCount projectMillis \n", - "0 2 4 74 " - ], - "text/html": [ - "\n", - "
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nodeQueryrelationshipQuerygraphNamenodeCountrelationshipCountprojectMillis
0MATCH (n:Entity) RETURN id(n) AS idMATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ...cypher_default_strategy2474
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nodeQueryrelationshipQuerygraphNamenodeCountrelationshipCountprojectMillis
0MATCH (n:Entity) RETURN id(n) AS idMATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ...cypher_default_strategy2474
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\n", + " " ], - "source": [ - "cypher_default_agg = \"\"\"\n", - "\n", - "CALL gds.graph.project.cypher('cypher_default_strategy', \n", - " 'MATCH (n:Entity) RETURN id(n) AS id', \n", - " 'MATCH (n:Entity)-[r]->(m:Entity)\n", - " RETURN id(n) AS source, id(m) AS target')\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(cypher_default_agg)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aOL1ttJ__9sf" - }, - "source": [ - "By looking at the relationshipCount, we observe that all four relationships have been projected as intended.To verify the projected graph, we run the degree centrality." + "text/plain": [ + " nodeQuery \\\n", + "0 MATCH (n:Entity) RETURN id(n) AS id \n", + "\n", + " relationshipQuery graphName \\\n", + "0 MATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ... cypher_default_strategy \n", + "\n", + " nodeCount relationshipCount projectMillis \n", + "0 2 4 74 " ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cypher_default_agg = \"\"\"\n", + "\n", + "CALL gds.graph.project.cypher('cypher_default_strategy', \n", + " 'MATCH (n:Entity) RETURN id(n) AS id', \n", + " 'MATCH (n:Entity)-[r]->(m:Entity)\n", + " RETURN id(n) AS source, id(m) AS target')\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(cypher_default_agg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aOL1ttJ__9sf" + }, + "source": [ + "By looking at the relationshipCount, we observe that all four relationships have been projected as intended.To verify the projected graph, we run the degree centrality." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 }, + "id": "Nx5mA6HB_9sg", + "outputId": "50f7e92a-30f7-4606-9045-cff5b00371ce" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "Nx5mA6HB_9sg", - "outputId": "50f7e92a-30f7-4606-9045-cff5b00371ce", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name degree\n", - "0 Tomaz 4.0\n", - "1 Neo4j 0.0" - ], - "text/html": [ - "\n", - "
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namedegree
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namedegree
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\n", + " " ], - "source": [ - "cypher_default_agg_check = \"\"\"\n", - "\n", - "CALL gds.degree.stream('cypher_default_strategy')\n", - "YIELD nodeId, score\n", - "RETURN gds.util.asNode(nodeId).name AS name,\n", - " score AS degree\n", - "ORDER BY degree DESC\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(cypher_default_agg_check)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "H9O2QwOh_9sg" - }, - "source": [ - "#### Single relationship strategy\n", - "\n", - "With cypher projection, we don't have access to relationship level aggregation strategies. This is no problem at all as it is very easy to reduce the multigraph to a single graph using only the cypher query language. We simply add the DISTINCT clause in the return of the relationship statement and it should be good to go." + "text/plain": [ + " name degree\n", + "0 Tomaz 4.0\n", + "1 Neo4j 0.0" ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cypher_default_agg_check = \"\"\"\n", + "\n", + "CALL gds.degree.stream('cypher_default_strategy')\n", + "YIELD nodeId, score\n", + "RETURN gds.util.asNode(nodeId).name AS name,\n", + " score AS degree\n", + "ORDER BY degree DESC\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(cypher_default_agg_check)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H9O2QwOh_9sg" + }, + "source": [ + "#### Single relationship strategy\n", + "\n", + "With cypher projection, we don't have access to relationship level aggregation strategies. This is no problem at all as it is very easy to reduce the multigraph to a single graph using only the cypher query language. We simply add the DISTINCT clause in the return of the relationship statement and it should be good to go." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "MwojiBmC_9sg", + "outputId": "3e0d5e51-3337-401e-d6ef-4e0c22b1f1ab" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "MwojiBmC_9sg", - "outputId": "3e0d5e51-3337-401e-d6ef-4e0c22b1f1ab", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeQuery \\\n", - "0 MATCH (n:Entity) RETURN id(n) AS id \n", - "\n", - " relationshipQuery graphName \\\n", - "0 MATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ... cypher_single_strategy \n", - "\n", - " nodeCount relationshipCount projectMillis \n", - "0 2 1 11 " - ], - "text/html": [ - "\n", - "
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nodeQueryrelationshipQuerygraphNamenodeCountrelationshipCountprojectMillis
0MATCH (n:Entity) RETURN id(n) AS idMATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ...cypher_single_strategy2111
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nodeQueryrelationshipQuerygraphNamenodeCountrelationshipCountprojectMillis
0MATCH (n:Entity) RETURN id(n) AS idMATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ...cypher_single_strategy2111
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\n", + " " ], - "source": [ - "cypher_single_agg = \"\"\"\n", - "\n", - "CALL gds.graph.project.cypher('cypher_single_strategy',\n", - " 'MATCH (n:Entity) RETURN id(n) AS id',\n", - " 'MATCH (n:Entity)-[r]->(m:Entity)\n", - " RETURN DISTINCT id(n) AS source, id(m) AS target' )\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(cypher_single_agg)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ueFYFcaD_9sg" - }, - "source": [ - "The relationship count is one, which means we have successfully reduced the multigraph. Remember to drop the projected graph." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "id": "RFwJ-_wK_9sg" - }, - "outputs": [], - "source": [ - "drop_graph('cypher_single_strategy')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5uc5pkhm_9sh" - }, - "source": [ - "#### Property aggregation strategies\n", - "\n", - "On the other hand, with cypher projection, we do have access to property level aggregation strategies. We don't really \"need\" them as we can accomplish all the transformation using only cypher. To show you what I mean by that, we can apply the minimum property strategy aggregation using plain cypher like:" + "text/plain": [ + " nodeQuery \\\n", + "0 MATCH (n:Entity) RETURN id(n) AS id \n", + "\n", + " relationshipQuery graphName \\\n", + "0 MATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ... cypher_single_strategy \n", + "\n", + " nodeCount relationshipCount projectMillis \n", + "0 2 1 11 " ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cypher_single_agg = \"\"\"\n", + "\n", + "CALL gds.graph.project.cypher('cypher_single_strategy',\n", + " 'MATCH (n:Entity) RETURN id(n) AS id',\n", + " 'MATCH (n:Entity)-[r]->(m:Entity)\n", + " RETURN DISTINCT id(n) AS source, id(m) AS target' )\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(cypher_single_agg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ueFYFcaD_9sg" + }, + "source": [ + "The relationship count is one, which means we have successfully reduced the multigraph. Remember to drop the projected graph." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "RFwJ-_wK_9sg" + }, + "outputs": [], + "source": [ + "drop_graph('cypher_single_strategy')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5uc5pkhm_9sh" + }, + "source": [ + "#### Property aggregation strategies\n", + "\n", + "On the other hand, with cypher projection, we do have access to property level aggregation strategies. We don't really \"need\" them as we can accomplish all the transformation using only cypher. To show you what I mean by that, we can apply the minimum property strategy aggregation using plain cypher like:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "wBSLIs1G_9sh", + "outputId": "a3147c91-3cef-49ea-8285-86f232bd01be" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "wBSLIs1G_9sh", - "outputId": "a3147c91-3cef-49ea-8285-86f232bd01be", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeQuery \\\n", - "0 MATCH (n:Entity) RETURN id(n) AS id \n", - "\n", - " relationshipQuery graphName \\\n", - "0 MATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ... cypher_min_strategy \n", - "\n", - " nodeCount relationshipCount projectMillis \n", - "0 2 1 66 " - ], - "text/html": [ - "\n", - "
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nodeQueryrelationshipQuerygraphNamenodeCountrelationshipCountprojectMillis
0MATCH (n:Entity) RETURN id(n) AS idMATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ...cypher_min_strategy2166
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nodeQueryrelationshipQuerygraphNamenodeCountrelationshipCountprojectMillis
0MATCH (n:Entity) RETURN id(n) AS idMATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ...cypher_min_strategy2166
\n", + "
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\n", + " " ], - "source": [ - "cypher_min_agg = \"\"\"\n", - "\n", - "CALL gds.graph.project.cypher('cypher_min_strategy', \n", - " 'MATCH (n:Entity) RETURN id(n) AS id', \n", - " 'MATCH (n:Entity)-[r]->(m:Entity)\n", - " RETURN id(n) AS source, id(m) AS target, min(r.weight) as weight' )\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(cypher_min_agg)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MJljp0hp_9si" - }, - "source": [ - "The relationshipCount is 1, which confirms our successful multigraph reduction. Just to make sure, we can run the weighted centrality and validate results." + "text/plain": [ + " nodeQuery \\\n", + "0 MATCH (n:Entity) RETURN id(n) AS id \n", + "\n", + " relationshipQuery graphName \\\n", + "0 MATCH (n:Entity)-[r]->(m:Entity)\\n RETURN ... cypher_min_strategy \n", + "\n", + " nodeCount relationshipCount projectMillis \n", + "0 2 1 66 " ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cypher_min_agg = \"\"\"\n", + "\n", + "CALL gds.graph.project.cypher('cypher_min_strategy', \n", + " 'MATCH (n:Entity) RETURN id(n) AS id', \n", + " 'MATCH (n:Entity)-[r]->(m:Entity)\n", + " RETURN id(n) AS source, id(m) AS target, min(r.weight) as weight' )\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(cypher_min_agg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MJljp0hp_9si" + }, + "source": [ + "The relationshipCount is 1, which confirms our successful multigraph reduction. Just to make sure, we can run the weighted centrality and validate results." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 }, + "id": "MJ6ufdjs_9si", + "outputId": "44bfb384-be37-486e-92ef-f59db29c544d" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "id": "MJ6ufdjs_9si", - "outputId": "44bfb384-be37-486e-92ef-f59db29c544d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name weighted_degree\n", - "0 Tomaz 0.5\n", - "1 Neo4j 0.0" - ], - "text/html": [ - "\n", - "
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\n", + " " ], - "source": [ - "cypher_min_agg_check = \"\"\"\n", - "\n", - "CALL gds.degree.stream('cypher_min_strategy',\n", - " {relationshipWeightProperty:'weight'})\n", - "YIELD nodeId, score \n", - "RETURN gds.util.asNode(nodeId).name AS name,\n", - " score AS weighted_degree\n", - "ORDER BY weighted_degree DESC\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(cypher_min_agg_check)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yjuGG1yE_9si" - }, - "source": [ - "With everything in order, we can release both projected graphs from memory." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "QYpV1JVN_9si" - }, - "outputs": [], - "source": [ - "drop_graph('cypher_min_strategy')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "quzNqWxS_9si" - }, - "source": [ - "## Relationships with own identity\n", - "\n", - "We also have the option to retain the type of relationships during the projection process. Among other things, this allows us to perform additional filtering when executing graph algorithms. However, we have to be careful, as projecting relationships with a preserved type is a bit different in the context of multigraphs.\n", - "\n", - "### Native projection\n", - "\n", - "It is simple to declare that we want to preserve the type of relationships with the native projection. All we have to do is specify which relationship types we want to consider and the GDS engine will automatically bundle relationships under the specific relationship type. Let's take a look at some examples to gain a better understanding.\n", - "\n", - "#### Default aggregation strategy\n", - "\n", - "From previous examples we already know that the default aggregation strategy does not perform any transformations. By defining the relationship types we indicate to the GDS library we want to retain their type after the projection process." + "text/plain": [ + " name weighted_degree\n", + "0 Tomaz 0.5\n", + "1 Neo4j 0.0" ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cypher_min_agg_check = \"\"\"\n", + "\n", + "CALL gds.degree.stream('cypher_min_strategy',\n", + " {relationshipWeightProperty:'weight'})\n", + "YIELD nodeId, score \n", + "RETURN gds.util.asNode(nodeId).name AS name,\n", + " score AS weighted_degree\n", + "ORDER BY weighted_degree DESC\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(cypher_min_agg_check)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yjuGG1yE_9si" + }, + "source": [ + "With everything in order, we can release both projected graphs from memory." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "QYpV1JVN_9si" + }, + "outputs": [], + "source": [ + "drop_graph('cypher_min_strategy')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "quzNqWxS_9si" + }, + "source": [ + "## Relationships with own identity\n", + "\n", + "We also have the option to retain the type of relationships during the projection process. Among other things, this allows us to perform additional filtering when executing graph algorithms. However, we have to be careful, as projecting relationships with a preserved type is a bit different in the context of multigraphs.\n", + "\n", + "### Native projection\n", + "\n", + "It is simple to declare that we want to preserve the type of relationships with the native projection. All we have to do is specify which relationship types we want to consider and the GDS engine will automatically bundle relationships under the specific relationship type. Let's take a look at some examples to gain a better understanding.\n", + "\n", + "#### Default aggregation strategy\n", + "\n", + "From previous examples we already know that the default aggregation strategy does not perform any transformations. By defining the relationship types we indicate to the GDS library we want to retain their type after the projection process." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "NR5j1_8b_9sj", + "outputId": "928299fd-fa9b-4a82-e1ad-39a81bfe17d8" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "NR5j1_8b_9sj", - "outputId": "928299fd-fa9b-4a82-e1ad-39a81bfe17d8", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeProjection \\\n", - "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", - "\n", - " relationshipProjection graphName nodeCount \\\n", - "0 {'LOVES': {'orientation': 'NATURAL', 'aggregat... type_default 2 \n", - "\n", - " relationshipCount projectMillis \n", - "0 4 69 " - ], - "text/html": [ - "\n", - "
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0{'__ALL__': {'label': '*', 'properties': {}}}{'LOVES': {'orientation': 'NATURAL', 'aggregat...type_default2469
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nodeProjectionrelationshipProjectiongraphNamenodeCountrelationshipCountprojectMillis
0{'__ALL__': {'label': '*', 'properties': {}}}{'LOVES': {'orientation': 'NATURAL', 'aggregat...type_default2469
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\n", + " " ], - "source": [ - "default_type = \"\"\"\n", - "\n", - "CALL gds.graph.project('type_default','*',\n", - " ['PRESENTED_FOR','LIKES','LOVES'])\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(default_type)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GpZed9nB_9sj" - }, - "source": [ - "As expected, the relationshipsCount is 4." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "id": "k-xNnrcS_9sj" - }, - "outputs": [], - "source": [ - "drop_graph('type_default')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HtlqhAnt_9sj" - }, - "source": [ - "#### Single relationship strategy\n", - "\n", - "Like before, we can reduce our unweighted multigraph to a single graph with the relationship level aggregation parameter. We have to provide the aggregation parameter for each relationship type separately." + "text/plain": [ + " nodeProjection \\\n", + "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", + "\n", + " relationshipProjection graphName nodeCount \\\n", + "0 {'LOVES': {'orientation': 'NATURAL', 'aggregat... type_default 2 \n", + "\n", + " relationshipCount projectMillis \n", + "0 4 69 " ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "default_type = \"\"\"\n", + "\n", + "CALL gds.graph.project('type_default','*',\n", + " ['PRESENTED_FOR','LIKES','LOVES'])\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(default_type)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GpZed9nB_9sj" + }, + "source": [ + "As expected, the relationshipsCount is 4." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "k-xNnrcS_9sj" + }, + "outputs": [], + "source": [ + "drop_graph('type_default')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HtlqhAnt_9sj" + }, + "source": [ + "#### Single relationship strategy\n", + "\n", + "Like before, we can reduce our unweighted multigraph to a single graph with the relationship level aggregation parameter. We have to provide the aggregation parameter for each relationship type separately." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "EOQJYnXo_9sj", + "outputId": "d0bed529-6f63-403d-9e5d-cbbf565eab3a" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "EOQJYnXo_9sj", - "outputId": "d0bed529-6f63-403d-9e5d-cbbf565eab3a", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeProjection \\\n", - "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", - "\n", - " relationshipProjection graphName nodeCount \\\n", - "0 {'LOVES': {'orientation': 'NATURAL', 'aggregat... type_single 2 \n", - "\n", - " relationshipCount projectMillis \n", - "0 3 75 " - ], - "text/html": [ - "\n", - "
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nodeProjectionrelationshipProjectiongraphNamenodeCountrelationshipCountprojectMillis
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nodeProjectionrelationshipProjectiongraphNamenodeCountrelationshipCountprojectMillis
0{'__ALL__': {'label': '*', 'properties': {}}}{'LOVES': {'orientation': 'NATURAL', 'aggregat...type_single2375
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\n", + " " ], - "source": [ - "type_single_agg = \"\"\"\n", - "\n", - "CALL gds.graph.project('type_single','*',\n", - " {LIKES:{type:'LIKES',aggregation:'SINGLE'},\n", - " LOVES:{type:'LOVES',aggregation:'SINGLE'},\n", - " PRESENTED_FOR:{type:'PRESENTED_FOR',aggregation:'SINGLE'}})\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(type_single_agg)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bM8ZNUR6_9sk" - }, - "source": [ - "Ok, so we reduced to a single graph, but the relationshipCount is 3. Why is it so? The multigraph reduction process works on the relationship type level and because we have three relationship types, a single relationship for each type has been projected. Let's calculate the degree centrality on the whole in-memory graph." + "text/plain": [ + " nodeProjection \\\n", + "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", + "\n", + " relationshipProjection graphName nodeCount \\\n", + "0 {'LOVES': {'orientation': 'NATURAL', 'aggregat... type_single 2 \n", + "\n", + " relationshipCount projectMillis \n", + "0 3 75 " ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type_single_agg = \"\"\"\n", + "\n", + "CALL gds.graph.project('type_single','*',\n", + " {LIKES:{type:'LIKES',aggregation:'SINGLE'},\n", + " LOVES:{type:'LOVES',aggregation:'SINGLE'},\n", + " PRESENTED_FOR:{type:'PRESENTED_FOR',aggregation:'SINGLE'}})\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(type_single_agg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bM8ZNUR6_9sk" + }, + "source": [ + "Ok, so we reduced to a single graph, but the relationshipCount is 3. Why is it so? The multigraph reduction process works on the relationship type level and because we have three relationship types, a single relationship for each type has been projected. Let's calculate the degree centrality on the whole in-memory graph." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 }, + "id": "1bNhlwcX_9sk", + "outputId": "329ca35c-6238-438a-d834-5ee3db070a13" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "id": "1bNhlwcX_9sk", - "outputId": "329ca35c-6238-438a-d834-5ee3db070a13", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name degree\n", - "0 Tomaz 3.0\n", - "1 Neo4j 0.0" - ], - "text/html": [ - "\n", - "
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\n", + " " ], - "source": [ - "type_single_agg_check = \"\"\"\n", - "\n", - "CALL gds.degree.stream('type_single')\n", - "YIELD nodeId, score\n", - "RETURN gds.util.asNode(nodeId).name AS name,\n", - " score AS degree\n", - "ORDER BY degree DESC\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(type_single_agg_check)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ue0b1p22_9sk" - }, - "source": [ - "As we explained, even though we have reduced each relationship type separately, we are still dealing with a multigraph on the whole. When running graph algorithms, you have to pay close attention to whether you are dealing with multigraph or not, have you projected multiple relationship types or just a single one and have you performed any transformations, as all of this will affect the algorithm results. We can now drop this graph." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "id": "35u12SFQ_9sk" - }, - "outputs": [], - "source": [ - "drop_graph('type_single')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2fiQSNz2_9sk" - }, - "source": [ - "#### Property aggregation strategies\n", - "\n", - "Property aggregation strategies are very similar to before when we were dealing with relationships without identity. The only change is that now the aggregations are grouped by the relationship type." + "text/plain": [ + " name degree\n", + "0 Tomaz 3.0\n", + "1 Neo4j 0.0" ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type_single_agg_check = \"\"\"\n", + "\n", + "CALL gds.degree.stream('type_single')\n", + "YIELD nodeId, score\n", + "RETURN gds.util.asNode(nodeId).name AS name,\n", + " score AS degree\n", + "ORDER BY degree DESC\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(type_single_agg_check)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ue0b1p22_9sk" + }, + "source": [ + "As we explained, even though we have reduced each relationship type separately, we are still dealing with a multigraph on the whole. When running graph algorithms, you have to pay close attention to whether you are dealing with multigraph or not, have you projected multiple relationship types or just a single one and have you performed any transformations, as all of this will affect the algorithm results. We can now drop this graph." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "35u12SFQ_9sk" + }, + "outputs": [], + "source": [ + "drop_graph('type_single')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2fiQSNz2_9sk" + }, + "source": [ + "#### Property aggregation strategies\n", + "\n", + "Property aggregation strategies are very similar to before when we were dealing with relationships without identity. The only change is that now the aggregations are grouped by the relationship type." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "FeFVkJZU_9sl", + "outputId": "02951c4e-7992-4808-a5fd-90330c9b50eb" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "id": "FeFVkJZU_9sl", - "outputId": "02951c4e-7992-4808-a5fd-90330c9b50eb", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeProjection \\\n", - "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", - "\n", - " relationshipProjection graphName nodeCount \\\n", - "0 {'LOVES': {'orientation': 'NATURAL', 'aggregat... type_min 2 \n", - "\n", - " relationshipCount projectMillis \n", - "0 3 113 " - ], - "text/html": [ - "\n", - "
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nodeProjectionrelationshipProjectiongraphNamenodeCountrelationshipCountprojectMillis
0{'__ALL__': {'label': '*', 'properties': {}}}{'LOVES': {'orientation': 'NATURAL', 'aggregat...type_min23113
\n", + "
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\n", + " " ], - "source": [ - "type_min_agg = \"\"\"\n", - "\n", - "CALL gds.graph.project('type_min','*',\n", - " ['PRESENTED_FOR','LIKES','LOVES'], \n", - " {relationshipProperties: {weight: {property: 'weight',\n", - " aggregation: 'MIN'}}})\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(type_min_agg)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gDsUSvVF_9sl" - }, - "source": [ - "We get 3 relationships projected as we have learned that the aggregations happen on the relationship type level. We will double-check the results with the weighted degree." + "text/plain": [ + " nodeProjection \\\n", + "0 {'__ALL__': {'label': '*', 'properties': {}}} \n", + "\n", + " relationshipProjection graphName nodeCount \\\n", + "0 {'LOVES': {'orientation': 'NATURAL', 'aggregat... type_min 2 \n", + "\n", + " relationshipCount projectMillis \n", + "0 3 113 " ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type_min_agg = \"\"\"\n", + "\n", + "CALL gds.graph.project('type_min','*',\n", + " ['PRESENTED_FOR','LIKES','LOVES'], \n", + " {relationshipProperties: {weight: {property: 'weight',\n", + " aggregation: 'MIN'}}})\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(type_min_agg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gDsUSvVF_9sl" + }, + "source": [ + "We get 3 relationships projected as we have learned that the aggregations happen on the relationship type level. We will double-check the results with the weighted degree." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 }, + "id": "yL9OMDT7_9sl", + "outputId": "91b71455-e1f8-4499-92d3-520359c442ec" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "id": "yL9OMDT7_9sl", - "outputId": "91b71455-e1f8-4499-92d3-520359c442ec", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " name weighted_degree\n", - "0 Tomaz 3.5\n", - "1 Neo4j 0.0" - ], - "text/html": [ - "\n", - "
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\n", + " " ], - "source": [ - "type_min_agg_check = \"\"\"\n", - "\n", - "CALL gds.degree.stream('type_min',\n", - " {relationshipWeightProperty:'weight'})\n", - "YIELD nodeId, score\n", - "RETURN gds.util.asNode(nodeId).name AS name,\n", - " score AS weighted_degree\n", - "ORDER BY weighted_degree DESC\n", - "\n", - "\"\"\"\n", - "\n", - "read_query(type_min_agg_check)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "id": "EWOo5l-3_9sl" - }, - "outputs": [], - "source": [ - "drop_graph('type_min')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7f-BZWXn_9sl" - }, - "outputs": [], - "source": [ - "" + "text/plain": [ + " name weighted_degree\n", + "0 Tomaz 3.5\n", + "1 Neo4j 0.0" ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - }, - "colab": { - "name": "GDS multigraph.ipynb", - "provenance": [], - "include_colab_link": true - } + ], + "source": [ + "type_min_agg_check = \"\"\"\n", + "\n", + "CALL gds.degree.stream('type_min',\n", + " {relationshipWeightProperty:'weight'})\n", + "YIELD nodeId, score\n", + "RETURN gds.util.asNode(nodeId).name AS name,\n", + " score AS weighted_degree\n", + "ORDER BY weighted_degree DESC\n", + "\n", + "\"\"\"\n", + "\n", + "read_query(type_min_agg_check)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "EWOo5l-3_9sl" + }, + "outputs": [], + "source": [ + "drop_graph('type_min')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7f-BZWXn_9sl" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "include_colab_link": true, + "name": "GDS multigraph.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Game_of_thrones_community_iteration/Game of thrones community iteration.ipynb b/Game_of_thrones_community_iteration/Game of thrones community iteration.ipynb index 0fe119c..68d8bc1 100644 --- a/Game_of_thrones_community_iteration/Game of thrones community iteration.ipynb +++ b/Game_of_thrones_community_iteration/Game of thrones community iteration.ipynb @@ -1,709 +1,655 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "* Updated to GDS 2.0 version\n", - "* Link to original blog post: https://towardsdatascience.com/community-detection-through-time-using-seed-property-in-neo4j-on-the-game-of-thrones-dataset-a2e520a6c79f" - ], - "metadata": { - "id": "_2CaCA2vDGbC" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install neo4j" - ], - "metadata": { - "id": "PG1voNyVDIfn", - "outputId": "ba912efc-e226-4e11-b840-a5959c3f7435", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting neo4j\n", - " Downloading neo4j-4.4.2.tar.gz (89 kB)\n", - "\u001b[?25l\r\u001b[K |███▋ | 10 kB 23.3 MB/s eta 0:00:01\r\u001b[K |███████▎ | 20 kB 12.3 MB/s eta 0:00:01\r\u001b[K |███████████ | 30 kB 8.8 MB/s eta 0:00:01\r\u001b[K |██████████████▋ | 40 kB 3.9 MB/s eta 0:00:01\r\u001b[K |██████████████████▎ | 51 kB 3.8 MB/s eta 0:00:01\r\u001b[K |██████████████████████ | 61 kB 4.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████████▋ | 71 kB 4.7 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▎ | 81 kB 4.9 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 89 kB 3.5 MB/s \n", - "\u001b[?25hRequirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j) (2018.9)\n", - "Building wheels for collected packages: neo4j\n", - " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for neo4j: filename=neo4j-4.4.2-py3-none-any.whl size=115365 sha256=4b6b3195024550cad7621ab4012c1b3dc9630377b161e72e6b622ee512cff2c6\n", - " Stored in directory: /root/.cache/pip/wheels/10/d6/28/95029d7f69690dbc3b93e4933197357987de34fbd44b50a0e4\n", - "Successfully built neo4j\n", - "Installing collected packages: neo4j\n", - "Successfully installed neo4j-4.4.2\n" - ] - } - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "gOf9wc5_CvHx" - }, - "outputs": [], - "source": [ - "from neo4j import GraphDatabase\n", - "host = 'bolt://3.235.2.228:7687'\n", - "user = 'neo4j'\n", - "password = 'seats-drunks-carbon'\n", - "driver = GraphDatabase.driver(host,auth=(user, password))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "id": "GZG8ZuroCvH1" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "def run_query(query, params={}):\n", - " with driver.session() as session:\n", - " result = session.run(query, params)\n", - " return pd.DataFrame([r.values() for r in result], columns=result.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "TfpDkC2aCvH2" - }, - "outputs": [], - "source": [ - "from IPython.display import IFrame, HTML\n", - "import json\n", - "import uuid\n", - "\n", - "\n", - "def generate_vis(host, user, password, cypher, labels_json, relationships_json):\n", - " html = \"\"\"\\\n", - "\n", - "\n", - " Neovis.js Simple Example\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \"\"\"\n", - "\n", - " html = html.format(\n", - " host=host,\n", - " user=user,\n", - " password=password,\n", - " cypher=cypher,\n", - " labels = json.dumps(labels_json),\n", - " relationships=json.dumps(relationships_json)\n", - " )\n", - "\n", - " unique_id = str(uuid.uuid4())\n", - " filename = \"graph-{}.html\".format(unique_id)\n", - "\n", - " with open(filename, \"w\") as f:\n", - " f.write(html)\n", - " return IFrame(src=filename, width=1000, height=800)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "7PBPz29RCvH3" - }, - "outputs": [], - "source": [ - "def visualize_level(level, community):\n", - " # Define cypher query\n", - " if level > 1:\n", - " cypher = \"\"\"MATCH (p1:Person)-[r:INTERACTS_{rel_level}|:INTERACTS_{prev_level}]-(p2:Person) \\\n", - " WHERE p1.community_{level} = {community} RETURN *\"\"\".format(\n", - " rel_level=level if level != 4 else 45,level=level, prev_level=level -1, community=community)\n", - " else:\n", - " cypher = \"\"\"MATCH (p1:Person)-[r:INTERACTS_{level}]-(p2:Person) \\\n", - " WHERE p1.community_{level} = {community} RETURN *\"\"\".format(level=level, community=community)\n", - " print(cypher)\n", - " # Define relationships_json\n", - " relationships_json = dict()\n", - " for l in [level-1,level]:\n", - " relationships_json[\"INTERACTS_{}\".format(l if l != 4 else 45)] = {\n", - " \"caption\": False\n", - " }\n", - " # Define labels_json \n", - " labels_json = {\n", - " \"Person\": {\n", - " \"caption\": \"id\",\n", - " \"community\": \"community_{}\".format(level)\n", - " }\n", - " }\n", - "\n", - " return generate_vis(host, user, password, cypher, labels_json, relationships_json)" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_2CaCA2vDGbC" + }, + "source": [ + "* Updated to GDS 2.3 version\n", + "* Link to original blog post: https://towardsdatascience.com/community-detection-through-time-using-seed-property-in-neo4j-on-the-game-of-thrones-dataset-a2e520a6c79f" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "PG1voNyVDIfn", + "outputId": "ba912efc-e226-4e11-b840-a5959c3f7435" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "SjP8vjfWCvH4" - }, - "source": [ - "# Import" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: neo4j in /home/tomaz/.local/lib/python3.8/site-packages (4.4.3)\r\n", + "Requirement already satisfied: pytz in /home/tomaz/anaconda3/lib/python3.8/site-packages (from neo4j) (2021.1)\r\n" + ] + } + ], + "source": [ + "!pip install neo4j" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "gOf9wc5_CvHx" + }, + "outputs": [], + "source": [ + "from neo4j import GraphDatabase\n", + "host = 'bolt://3.231.25.240:7687'\n", + "user = 'neo4j'\n", + "password = 'hatchets-visitor-axes'\n", + "driver = GraphDatabase.driver(host,auth=(user, password))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "GZG8ZuroCvH1" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "def run_query(query, params={}):\n", + " with driver.session() as session:\n", + " result = session.run(query, params)\n", + " return pd.DataFrame([r.values() for r in result], columns=result.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "TfpDkC2aCvH2" + }, + "outputs": [], + "source": [ + "from IPython.display import IFrame, HTML\n", + "import json\n", + "import uuid\n", + "\n", + "\n", + "def generate_vis(host, user, password, cypher, labels_json, relationships_json):\n", + " html = \"\"\"\\\n", + " \n", + " \n", + " Neovis.js Simple Example\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " \"\"\"\n", + "\n", + " html = html.format(\n", + " host=host,\n", + " user=user,\n", + " password=password,\n", + " cypher=cypher,\n", + " labels = json.dumps(labels_json),\n", + " relationships=json.dumps(relationships_json)\n", + " )\n", + "\n", + " unique_id = str(uuid.uuid4())\n", + " filename = \"graph-{}.html\".format(unique_id)\n", + "\n", + " with open(filename, \"w\") as f:\n", + " f.write(html)\n", + " return IFrame(src=filename, width=1000, height=800)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "7PBPz29RCvH3" + }, + "outputs": [], + "source": [ + "def visualize_level(level, community):\n", + " # Define cypher query\n", + " if level > 1:\n", + " cypher = \"\"\"MATCH (p1:Person)-[r:INTERACTS_{rel_level}|INTERACTS_{prev_level}]-(p2:Person) \\\n", + " WHERE p1.community_{level} = {community} RETURN *\"\"\".format(\n", + " rel_level=level if level != 4 else 45,level=level, prev_level=level -1, community=community)\n", + " else:\n", + " cypher = \"\"\"MATCH (p1:Person)-[r:INTERACTS_{level}]-(p2:Person) \\\n", + " WHERE p1.community_{level} = {community} RETURN *\"\"\".format(level=level, community=community)\n", + " print(cypher)\n", + " # Define relationships_json\n", + " relationships_json = dict()\n", + " for l in [level-1,level]:\n", + " relationships_json[\"INTERACTS_{}\".format(l if l != 4 else 45)] = {\n", + " \"caption\": False\n", + " }\n", + " # Define labels_json \n", + " labels_json = {\n", + " \"Person\": {\n", + " \"label\": \"id\",\n", + " \"group\": \"community_{}\".format(level)\n", + " }\n", + " }\n", + "\n", + " return generate_vis(host, user, password, cypher, labels_json, relationships_json)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SjP8vjfWCvH4" + }, + "source": [ + "# Import" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49 }, + "id": "udyBibdJCvH5", + "outputId": "0cc93822-b5ac-4cc4-e60b-b1689ccfa7bd" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "id": "udyBibdJCvH5", - "outputId": "0cc93822-b5ac-4cc4-e60b-b1689ccfa7bd", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: []" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "constraint_query = \"\"\"CREATE CONSTRAINT IF NOT EXISTS ON (p:Person) ASSERT p.id IS UNIQUE;\"\"\"\n", - "run_query(constraint_query)" + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "DEcCdTlNCvH6" - }, - "outputs": [], - "source": [ - "# https://networkofthrones.wordpress.com/\n", - "import_networks = \"\"\"\n", - "\n", - "UNWIND ['1','2','3','45'] as book\n", - "LOAD CSV WITH HEADERS FROM \n", - "'https://raw.githubusercontent.com/mathbeveridge/asoiaf/master/data/asoiaf-book' + book + '-edges.csv' as value\n", - "MERGE (source:Person{id:value.Source})\n", - "MERGE (target:Person{id:value.Target})\n", - "WITH source,target,value.weight as weight,book\n", - "CALL apoc.merge.relationship(source,'INTERACTS_' + book, {}, {weight:toFloat(weight)}, target) YIELD rel\n", - "RETURN distinct 'done'\n", - "\n", - "\"\"\"\n", - "run_query(import_networks)" - ] - }, + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "constraint_query = \"\"\"CREATE CONSTRAINT IF NOT EXISTS FOR (p:Person) REQUIRE p.id IS UNIQUE;\"\"\"\n", + "run_query(constraint_query)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "DEcCdTlNCvH6" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "def write_louvain(book):\n", - " project_graph_query = f\"\"\"\n", - " CALL gds.graph.project.cypher('book',\n", - " 'MATCH (p:Person)\n", - " WHERE (p)-[:INTERACTS_{book}]-()\n", - " RETURN id(p) as id',\n", - " 'MATCH (p:Person)-[:INTERACTS_{book}]-(p1:Person)\n", - " RETURN id(p) as source, id(p1) as target')\n", - "\"\"\"\n", - "\n", - " louvain_book = f\"\"\"\n", - " CALL gds.louvain.write('book'\n", - " ,{{writeProperty:'community_{book}'}})\n", - " \"\"\"\n", - "\n", - " drop_graph = \"\"\"\n", - " CALL gds.graph.drop('book')\n", - " \"\"\"\n", - " run_query(project_graph_query)\n", - " run_query(louvain_book)\n", - " run_query(drop_graph)" + "data": { + "text/html": [ + "
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" ], - "metadata": { - "id": "g4-CauAHEIIo" - }, - "execution_count": 32, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rtmxB-ytCvH7" - }, - "source": [ - "# Book 1" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "id": "ZTtOdPv-CvH8" - }, - "outputs": [], - "source": [ - "write_louvain(\"1\")" + "text/plain": [ + " 'done'\n", + "0 done" ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# https://networkofthrones.wordpress.com/\n", + "import_networks = \"\"\"\n", + "\n", + "UNWIND ['1','2','3','45'] as book\n", + "LOAD CSV WITH HEADERS FROM \n", + "'https://raw.githubusercontent.com/mathbeveridge/asoiaf/master/data/asoiaf-book' + book + '-edges.csv' as value\n", + "MERGE (source:Person{id:value.Source})\n", + "MERGE (target:Person{id:value.Target})\n", + "WITH source,target,value.weight as weight,book\n", + "CALL apoc.merge.relationship(source,'INTERACTS_' + book, {}, {weight:toFloat(weight)}, target) YIELD rel\n", + "RETURN distinct 'done'\n", + "\n", + "\"\"\"\n", + "run_query(import_networks)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "g4-CauAHEIIo" + }, + "outputs": [], + "source": [ + "def write_louvain(book):\n", + " project_graph_query = f\"\"\"\n", + " CALL gds.graph.project.cypher('book',\n", + " 'MATCH (p:Person)\n", + " WHERE (p)-[:INTERACTS_{book}]-()\n", + " RETURN id(p) as id',\n", + " 'MATCH (p:Person)-[:INTERACTS_{book}]-(p1:Person)\n", + " RETURN id(p) as source, id(p1) as target')\n", + "\"\"\"\n", + "\n", + " louvain_book = f\"\"\"\n", + " CALL gds.louvain.write('book'\n", + " ,{{writeProperty:'community_{book}'}})\n", + " \"\"\"\n", + "\n", + " drop_graph = \"\"\"\n", + " CALL gds.graph.drop('book')\n", + " \"\"\"\n", + " run_query(project_graph_query)\n", + " run_query(louvain_book)\n", + " run_query(drop_graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rtmxB-ytCvH7" + }, + "source": [ + "# Book 1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "ZTtOdPv-CvH8" + }, + "outputs": [], + "source": [ + "write_louvain(\"1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "Hr2r-5U1CvH8" + }, + "outputs": [], + "source": [ + "# Get Daenerys' community id \n", + "get_daenerys_community_query = \"\"\"\n", + "MATCH (p:Person{id:'Daenerys-Targaryen'})\n", + "RETURN p.community_1 as community\n", + "\"\"\"\n", + "\n", + "daenerys_community = run_query(get_daenerys_community_query)['community'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 839 }, + "id": "jTi8TtCUCvH9", + "outputId": "2d6073df-7ed1-45e5-be44-2369660652cd" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "id": "Hr2r-5U1CvH8" - }, - "outputs": [], - "source": [ - "# Get Daenerys' community id \n", - "get_daenerys_community_query = \"\"\"\n", - "MATCH (p:Person{id:'Daenerys-Targaryen'})\n", - "RETURN p.community_1 as community\n", - "\"\"\"\n", - "\n", - "daenerys_community = run_query(get_daenerys_community_query)['community'][0]" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MATCH (p1:Person)-[r:INTERACTS_1]-(p2:Person) WHERE p1.community_1 = 52 RETURN *\n" + ] }, { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "id": "jTi8TtCUCvH9", - "outputId": "2d6073df-7ed1-45e5-be44-2369660652cd", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 839 - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "MATCH (p1:Person)-[r:INTERACTS_1]-(p2:Person) WHERE p1.community_1 = 52 RETURN *\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {}, - "execution_count": 38 - } + "data": { + "text/html": [ + "\n", + " \n", + " " ], - "source": [ - "visualize_level(level=1,community=daenerys_community)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ye6-xWwdCvH-" - }, - "source": [ - "# Book 2" + "text/plain": [ + "" ] - }, + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "visualize_level(level=1,community=daenerys_community)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ye6-xWwdCvH-" + }, + "source": [ + "# Book 2" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "SkCJduILCvH-" + }, + "outputs": [], + "source": [ + "write_louvain(\"2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "nTNS2AK1CvH-", + "outputId": "b155bc72-1b22-4609-b915-c34314c21408" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "id": "SkCJduILCvH-" - }, - "outputs": [], - "source": [ - "write_louvain(\"2\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MATCH (p1:Person)-[r:INTERACTS_2|INTERACTS_1]-(p2:Person) WHERE p1.community_2 = 52 RETURN *\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "nTNS2AK1CvH-", - "outputId": "b155bc72-1b22-4609-b915-c34314c21408" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MATCH (p1:Person)-[r:INTERACTS_2|:INTERACTS_1]-(p2:Person) WHERE p1.community_2 = 3 RETURN *\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "\n", + " \n", + " " ], - "source": [ - "visualize_level(level=2,community=daenerys_community)" + "text/plain": [ + "" ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "niEQO5pwCvH_" - }, - "source": [ - "# Book 3" - ] - }, + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "visualize_level(level=2,community=daenerys_community)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "niEQO5pwCvH_" + }, + "source": [ + "# Book 3" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "MIITSfdACvH_" + }, + "outputs": [], + "source": [ + "write_louvain(\"3\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "K0GJcsFqCvH_", + "outputId": "36d8e774-d0ec-4a4d-f9f0-898f867c851f" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "id": "MIITSfdACvH_" - }, - "outputs": [], - "source": [ - "write_louvain(\"3\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MATCH (p1:Person)-[r:INTERACTS_3|INTERACTS_2]-(p2:Person) WHERE p1.community_3 = 52 RETURN *\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "K0GJcsFqCvH_", - "outputId": "36d8e774-d0ec-4a4d-f9f0-898f867c851f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MATCH (p1:Person)-[r:INTERACTS_3|:INTERACTS_2]-(p2:Person) WHERE p1.community_3 = 3 RETURN *\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "\n", + " \n", + " " ], - "source": [ - "visualize_level(level=3,community=daenerys_community)" + "text/plain": [ + "" ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zCadSySZCvIA" - }, - "source": [ - "# Book 4" - ] - }, + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "visualize_level(level=3,community=daenerys_community)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zCadSySZCvIA" + }, + "source": [ + "# Book 4" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "B5wN4bd4CvIA" + }, + "outputs": [], + "source": [ + "write_louvain(\"45\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "Tc4FVKVTCvIA", + "outputId": "afc17880-1ac3-4428-83c5-fbe8eb332517" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "id": "B5wN4bd4CvIA" - }, - "outputs": [], - "source": [ - "write_louvain(\"45\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "MATCH (p1:Person)-[r:INTERACTS_45|INTERACTS_3]-(p2:Person) WHERE p1.community_4 = 52 RETURN *\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Tc4FVKVTCvIA", - "outputId": "afc17880-1ac3-4428-83c5-fbe8eb332517" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MATCH (p1:Person)-[r:INTERACTS_45|:INTERACTS_3]-(p2:Person) WHERE p1.community_4 = 3 RETURN *\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "\n", + " \n", + " " ], - "source": [ - "visualize_level(level=4,community=daenerys_community)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "uWD1_qV3CvIA" - }, - "outputs": [], - "source": [ - "" + "text/plain": [ + "" ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - }, - "colab": { - "name": "Game of thrones community iteration.ipynb", - "provenance": [], - "include_colab_link": true - } + ], + "source": [ + "visualize_level(level=4,community=daenerys_community)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uWD1_qV3CvIA" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "include_colab_link": true, + "name": "Game of thrones community iteration.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Lord_of_the_wikidata/Part1 Importing Wikidata into Neo4j and analyzing family trees.ipynb b/Lord_of_the_wikidata/Part1 Importing Wikidata into Neo4j and analyzing family trees.ipynb index ebdcf1c..e5e93be 100644 --- a/Lord_of_the_wikidata/Part1 Importing Wikidata into Neo4j and analyzing family trees.ipynb +++ b/Lord_of_the_wikidata/Part1 Importing Wikidata into Neo4j and analyzing family trees.ipynb @@ -1,4401 +1,2702 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "* Updated to GDS 2.0 version\n", - "* Link to original blog post: https://towardsdatascience.com/lord-of-the-wiki-ring-importing-wikidata-into-neo4j-and-analyzing-family-trees-da27f64d675e" - ], - "metadata": { - "id": "Hwk0pHemHeWt" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install neo4j" - ], - "metadata": { - "id": "8aH7cn62Hn3g", - "outputId": "8625415d-c9c4-4e3c-85d9-9c3ae3055c43", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting neo4j\n", - " Downloading neo4j-4.4.2.tar.gz (89 kB)\n", - "\u001b[?25l\r\u001b[K |███▋ | 10 kB 24.3 MB/s eta 0:00:01\r\u001b[K |███████▎ | 20 kB 14.7 MB/s eta 0:00:01\r\u001b[K |███████████ | 30 kB 10.6 MB/s eta 0:00:01\r\u001b[K |██████████████▋ | 40 kB 9.2 MB/s eta 0:00:01\r\u001b[K |██████████████████▎ | 51 kB 4.6 MB/s eta 0:00:01\r\u001b[K |██████████████████████ | 61 kB 5.4 MB/s eta 0:00:01\r\u001b[K |█████████████████████████▋ | 71 kB 5.8 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▎ | 81 kB 5.7 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 89 kB 3.3 MB/s \n", - "\u001b[?25hRequirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j) (2018.9)\n", - "Building wheels for collected packages: neo4j\n", - " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for neo4j: filename=neo4j-4.4.2-py3-none-any.whl size=115365 sha256=1263c50dc3a5bf370b9d96525936014a00881f6b44f5d41da992312297966fac\n", - " Stored in directory: /root/.cache/pip/wheels/10/d6/28/95029d7f69690dbc3b93e4933197357987de34fbd44b50a0e4\n", - "Successfully built neo4j\n", - "Installing collected packages: neo4j\n", - "Successfully installed neo4j-4.4.2\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "I recommend you setup a [blank project on Neo4j Sandbox environment](https://sandbox.neo4j.com/?usecase=blank-sandbox), but you can also use other environment versions" - ], - "metadata": { - "id": "l9PMy6mJHpvZ" - } - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "IDQFrF1OHa4C" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "# Define Neo4j connections\n", - "from neo4j import GraphDatabase\n", - "host = 'bolt://3.235.2.228:7687'\n", - "user = 'neo4j'\n", - "password = 'seats-drunks-carbon'\n", - "driver = GraphDatabase.driver(host,auth=(user, password))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "OyQ7nwshHa4G" - }, - "outputs": [], - "source": [ - "# Import libraries\n", - "import pandas as pd\n", - "\n", - "def run_query(query, params={}):\n", - " with driver.session() as session:\n", - " result = session.run(query, params)\n", - " return pd.DataFrame([r.values() for r in result], columns=result.keys())" - ] - }, - { - "cell_type": "code", - "source": [ - "# Fix default timeout query setting in Sandbox\n", - "\n", - "run_query(\"\"\"\n", - "CALL dbms.setConfigValue('dbms.transaction.timeout','0')\n", - "\"\"\")" - ], - "metadata": { - "id": "Yox2IsDmNuD3", - "outputId": "88ff6dba-15f2-4293-a5a3-91eef0c8ef06", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49 - } - }, - "execution_count": 25, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: []" - ], - "text/html": [ - "\n", - "
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On top of that, we also know if they are part of any group or have participated in any event.\n", - "\n", - "## WikiData import\n", - "\n", - "As mentioned, we will fetch the data from the WikiData API with the help of the apoc.load.json procedure. If you don't know yet, APOC provides great support for importing data into Neo4j. Besides the ability to fetch data from any REST API, it also features integrations with other databases such as MongoDB or relational databases via the JDBC driver.\n", - "\n", - "P.s. You should check out Neosematics library if you work a lot with RDF data, I only noticed it after I have written the post\n", - "\n", - "We will start by importing all the races in the LOTR world. I have to admit I am a total noob when it comes to SPARQL, so I won't be explaining the syntax in depth. If you need a basic introduction on how to query WikiData, I suggest this tutorial on Youtube. Basically, all the races in the LOTR world are an instance of the Middle-earth races entity with id Q989255. To get the instances of a specific entity, we use the following SPARQL clause:\n", - "\n", - "?item wdt:P31 wd:Q989255\n", - "\n", - "This can be translated as \"We would like to fetch an item, which is an instance of (wdt:P31) an entity with an id Q989255\". After we have downloaded the data with APOC, we store the results to Neo4j." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "id": "6t_-UwQ4Ha4J" - }, - "outputs": [], - "source": [ - "import_races_query = \"\"\"\n", - "\n", - "// Prepare a SPARQL query \n", - "WITH 'SELECT ?item ?itemLabel WHERE{ ?item wdt:P31 wd:Q989255 . SERVICE wikibase:label { bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en\" }}' AS sparql \n", - "// make a request to Wikidata\n", - "CALL apoc.load.jsonParams('https://query.wikidata.org/sparql?query=' + \n", - " sparql, \n", - " { Accept: \"application/sparql-results+json\"}, null) \n", - "YIELD value \n", - "// Unwind results to row \n", - "UNWIND value['results']['bindings'] as row \n", - "// Prepare data \n", - "WITH row['itemLabel']['value'] as race, \n", - " row['item']['value'] as url, \n", - " split(row['item']['value'],'/')[-1] as id \n", - "// Store to Neo4j \n", - "CREATE (r:Race) SET r.race = race, \n", - " r.url = url, \n", - " r.id = id\n", - "\n", - "\"\"\"\n", - "\n", - "r = run_query(import_races_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SJSd4ON_Ha4L" - }, - "source": [ - "That was easy. The next step is to fetch the characters that are an instance of a given Middle-earth race. The SPARQL syntax is almost identical to the previous query, except this time we iterate over each race and find the characters that are an instance of a given race." - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Hwk0pHemHeWt" + }, + "source": [ + "* Updated to GDS 2.0 version\n", + "* Link to original blog post: https://towardsdatascience.com/lord-of-the-wiki-ring-importing-wikidata-into-neo4j-and-analyzing-family-trees-da27f64d675e" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "8aH7cn62Hn3g", + "outputId": "8625415d-c9c4-4e3c-85d9-9c3ae3055c43" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "Q6d0zuc7Ha4L" - }, - "outputs": [], - "source": [ - "import_characters_query = \"\"\"\n", - "\n", - "// Iterate over each race in graph\n", - "MATCH (r:Race)\n", - "// Prepare a SparQL query\n", - "WITH 'SELECT ?item ?itemLabel WHERE { ?item wdt:P31 wd:' + r.id + ' . SERVICE wikibase:label { bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en\" } }' AS sparql, r \n", - "// make a request to Wikidata \n", - "CALL apoc.load.jsonParams( \"https://query.wikidata.org/sparql?query=\" + \n", - " sparql, \n", - " { Accept: \"application/sparql-results+json\"}, null)\n", - "YIELD value \n", - "UNWIND value['results']['bindings'] as row \n", - "WITH row['itemLabel']['value'] as name, \n", - " row['item']['value'] as url, \n", - " split(row['item']['value'],'/')[-1] as id, r \n", - "// Store to Neo4j \n", - "CREATE (c:Character) \n", - "SET c.name = name, \n", - " c.url = url, \n", - " c.id = id \n", - "CREATE (c)-[:BELONG_TO]->(r)\n", - "\n", - "\"\"\"\n", - "\n", - "r = run_query(import_characters_query)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting neo4j\n", + " Downloading neo4j-4.4.2.tar.gz (89 kB)\n", + "\u001b[?25l\r", + "\u001b[K |███▋ | 10 kB 24.3 MB/s eta 0:00:01\r", + "\u001b[K |███████▎ | 20 kB 14.7 MB/s eta 0:00:01\r", + "\u001b[K |███████████ | 30 kB 10.6 MB/s eta 0:00:01\r", + "\u001b[K |██████████████▋ | 40 kB 9.2 MB/s eta 0:00:01\r", + "\u001b[K |██████████████████▎ | 51 kB 4.6 MB/s eta 0:00:01\r", + "\u001b[K |██████████████████████ | 61 kB 5.4 MB/s eta 0:00:01\r", + "\u001b[K |█████████████████████████▋ | 71 kB 5.8 MB/s eta 0:00:01\r", + "\u001b[K |█████████████████████████████▎ | 81 kB 5.7 MB/s eta 0:00:01\r", + "\u001b[K |████████████████████████████████| 89 kB 3.3 MB/s \n", + "\u001b[?25hRequirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j) (2018.9)\n", + "Building wheels for collected packages: neo4j\n", + " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for neo4j: filename=neo4j-4.4.2-py3-none-any.whl size=115365 sha256=1263c50dc3a5bf370b9d96525936014a00881f6b44f5d41da992312297966fac\n", + " Stored in directory: /root/.cache/pip/wheels/10/d6/28/95029d7f69690dbc3b93e4933197357987de34fbd44b50a0e4\n", + "Successfully built neo4j\n", + "Installing collected packages: neo4j\n", + "Successfully installed neo4j-4.4.2\n" + ] + } + ], + "source": [ + "!pip install neo4j" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l9PMy6mJHpvZ" + }, + "source": [ + "I recommend you setup a [blank project on Neo4j Sandbox environment](https://sandbox.neo4j.com/?usecase=blank-sandbox), but you can also use other environment versions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "IDQFrF1OHa4C" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "# Define Neo4j connections\n", + "from neo4j import GraphDatabase\n", + "host = 'bolt://44.193.28.203:7687'\n", + "user = 'neo4j'\n", + "password = 'combatants-coordinates-tugs'\n", + "driver = GraphDatabase.driver(host,auth=(user, password))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "OyQ7nwshHa4G" + }, + "outputs": [], + "source": [ + "# Import libraries\n", + "import pandas as pd\n", + "\n", + "def run_query(query, params={}):\n", + " with driver.session() as session:\n", + " result = session.run(query, params)\n", + " return pd.DataFrame([r.values() for r in result], columns=result.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49 }, - { - "cell_type": "markdown", - "metadata": { - "id": "m3vghrJ-Ha4M" - }, - "source": [ - "Did you know that there are at least 700 characters in the Middle-earth world? I would never guess there would be so many documented characters on WikiData. Our first exploratory cypher query will be to count the characters by race." - ] + "id": "Yox2IsDmNuD3", + "outputId": "88ff6dba-15f2-4293-a5a3-91eef0c8ef06" + }, + "outputs": [], + "source": [ + "# Fix default timeout query setting in Sandbox\n", + "\n", + "run_query(\"\"\"\n", + "CALL dbms.setConfigValue('dbms.transaction.timeout','0')\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g_gvwAYQHa4H" + }, + "source": [ + "## Agenda\n", + "\n", + "* Import Wikipedia data to Neo4j\n", + "* Basic graph exploration\n", + "* Populate missing value\n", + "* Some more graph exploration\n", + "* Weakly connected component\n", + "* Betweenness centrality\n", + "\n", + "We have been using simple graph schemas for quite some time now. I am delighted to say that this time we have a bit more complicated schema. The graph schema revolves around the characters in the LOTR world. A character can be either a relative, father, mother, enemy, spouse, or sibling with another character. This represents a social network of characters with multiple types of relationships. We also have additional information about characters such as their race, country, and language. On top of that, we also know if they are part of any group or have participated in any event.\n", + "\n", + "## WikiData import\n", + "\n", + "As mentioned, we will fetch the data from the WikiData API with the help of the apoc.load.json procedure. If you don't know yet, APOC provides great support for importing data into Neo4j. Besides the ability to fetch data from any REST API, it also features integrations with other databases such as MongoDB or relational databases via the JDBC driver.\n", + "\n", + "P.s. You should check out Neosematics library if you work a lot with RDF data, I only noticed it after I have written the post\n", + "\n", + "We will start by importing all the races in the LOTR world. I have to admit I am a total noob when it comes to SPARQL, so I won't be explaining the syntax in depth. If you need a basic introduction on how to query WikiData, I suggest this tutorial on Youtube. Basically, all the races in the LOTR world are an instance of the Middle-earth races entity with id Q989255. To get the instances of a specific entity, we use the following SPARQL clause:\n", + "\n", + "?item wdt:P31 wd:Q989255\n", + "\n", + "This can be translated as \"We would like to fetch an item, which is an instance of (wdt:P31) an entity with an id Q989255\". After we have downloaded the data with APOC, we store the results to Neo4j." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "6t_-UwQ4Ha4J" + }, + "outputs": [], + "source": [ + "import_races_query = \"\"\"\n", + "\n", + "// Prepare a SPARQL query \n", + "WITH 'SELECT ?item ?itemLabel WHERE{ ?item wdt:P31 wd:Q989255 . SERVICE wikibase:label { bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en\" }}' AS sparql \n", + "// make a request to Wikidata\n", + "CALL apoc.load.jsonParams('https://query.wikidata.org/sparql?query=' + \n", + " apoc.text.urlencode(sparql), \n", + " { Accept: \"application/sparql-results+json\"}, null) \n", + "YIELD value \n", + "// Unwind results to row \n", + "UNWIND value['results']['bindings'] as row \n", + "// Prepare data \n", + "WITH row['itemLabel']['value'] as race, \n", + " row['item']['value'] as url, \n", + " split(row['item']['value'],'/')[-1] as id \n", + "// Store to Neo4j \n", + "CREATE (r:Race) SET r.race = race, \n", + " r.url = url, \n", + " r.id = id\n", + "\n", + "\"\"\"\n", + "\n", + "r = run_query(import_races_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SJSd4ON_Ha4L" + }, + "source": [ + "That was easy. The next step is to fetch the characters that are an instance of a given Middle-earth race. The SPARQL syntax is almost identical to the previous query, except this time we iterate over each race and find the characters that are an instance of a given race." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "Q6d0zuc7Ha4L" + }, + "outputs": [], + "source": [ + "import_characters_query = \"\"\"\n", + "\n", + "// Iterate over each race in graph\n", + "MATCH (r:Race)\n", + "// Prepare a SparQL query\n", + "WITH 'SELECT ?item ?itemLabel WHERE { ?item wdt:P31 wd:' + r.id + ' . SERVICE wikibase:label { bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en\" } }' AS sparql, r \n", + "// make a request to Wikidata \n", + "CALL apoc.load.jsonParams( \"https://query.wikidata.org/sparql?query=\" + \n", + " apoc.text.urlencode(sparql), \n", + " { Accept: \"application/sparql-results+json\"}, null)\n", + "YIELD value \n", + "UNWIND value['results']['bindings'] as row \n", + "WITH row['itemLabel']['value'] as name, \n", + " row['item']['value'] as url, \n", + " split(row['item']['value'],'/')[-1] as id, r \n", + "// Store to Neo4j \n", + "CREATE (c:Character) \n", + "SET c.name = name, \n", + " c.url = url, \n", + " c.id = id \n", + "CREATE (c)-[:BELONG_TO]->(r)\n", + "\n", + "\"\"\"\n", + "\n", + "r = run_query(import_characters_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m3vghrJ-Ha4M" + }, + "source": [ + "Did you know that there are at least 700 characters in the Middle-earth world? I would never guess there would be so many documented characters on WikiData. Our first exploratory cypher query will be to count the characters by race." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 }, + "id": "QZYwABSvHa4N", + "outputId": "c0598e39-6598-4d63-9fd6-b57902e9b876" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "id": "QZYwABSvHa4N", - "outputId": "c0598e39-6598-4d63-9fd6-b57902e9b876", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " race members\n", - "0 men in Tolkien's legendarium 345\n", - "1 Hobbit 150\n", - "2 Middle-earth elf 83\n", - "3 dwarves in Tolkien's legendarium 52\n", - "4 valar 16\n", - "5 half-elven 12\n", - "6 Maiar 10\n", - "7 Orcs in Tolkien's legendarium 9\n", - "8 Ent 5\n", - "9 dragons of Middle-earth 4" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "race_size_query = \"\"\"\n", - "\n", - "MATCH (r:Race) \n", - "RETURN r.race as race, \n", - " size((r)<-[:BELONG_TO]-()) as members \n", - "ORDER BY members DESC \n", - "LIMIT 10\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(race_size_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UazugeKjHa4N" - }, - "source": [ - "The Fellowship of the Ring group is a somewhat representative sample of races in the Middle-earth. Most of the characters are either human or hobbits, with a couple of elves and dwarves strolling by. This is the first time I have heard of Valar and Maiar races though.\n", - "\n", - "Now it is time to enrich the graph with information about characters' gender, country, and manner of death. The SPARQL query will be a bit different than before. This time we will select a WikiData entity directly by its unique id and optionally fetch some of its properties. We can filter a specific entity by its id using the following SPARQL clause:\n", - "\n", - "filter (?item = wd:' + r.id + ')\n", - "\n", - "Similar to the cypher query language, SPARQL also differentiates between a MATCH and an OPTIONAL MATCH. When we want to return multiple properties of an entity, it is best to wrap each property into an OPTIONAL MATCH. This way we will get results if any of the properties exist. Without the OPTIONAL MATCH, we would only get results for entities where all three properties exist. This is an identical behavior to cypher.\n", - "\n", - "OPTIONAL{ ?item wdt:P21 [rdfs:label ?gender] . \n", - " filter (lang(?gender)=\"en\") }\n", - "\n", - "The wdt:P21 indicates we are interested in the gender property.  We also specify that we want to get the English label of an entity instead of its WikiData id. The easiest way to search for the desired property id is to inspect the entity on the WikiData web page and hover over a property name.\n", - "\n", - "Another way is to use the WikiData query editor, which has a great autocomplete function by using the CTRL+T command.\n", - "\n", - "To store the results back to Neo4j we will use the FOREACH trick. Because some of our results will contain null values, we have to wrap the MERGE statement into the FOREACH statement which supports conditional execution. Check the Tips and tricks blog post by Michael Hunger for more information." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "id": "l3lj59OdHa4O" - }, - "outputs": [], - "source": [ - "import_gender_query = \"\"\"\n", - "\n", - "// Iterate over characters \n", - "MATCH (r:Character) \n", - "// Prepare a SparQL query \n", - "WITH 'SELECT * WHERE{ ?item rdfs:label ?name . filter (?item = wd:' + r.id + ') filter (lang(?name) = \"en\" ) . ' +\n", - " 'OPTIONAL{ ?item wdt:P21 [rdfs:label ?gender] . filter (lang(?gender)=\"en\") } ' + \n", - " 'OPTIONAL{ ?item wdt:P27 [rdfs:label ?country] . filter (lang(?country)=\"en\") } ' +\n", - " 'OPTIONAL{ ?item wdt:P1196 [rdfs:label ?death] . filter (lang(?death)=\"en\") }}' AS sparql, r \n", - "// make a request to Wikidata \n", - "CALL apoc.load.jsonParams( \"https://query.wikidata.org/sparql?query=\" \n", - " + sparql, \n", - " { Accept: \"application/sparql-results+json\"}, null)\n", - "YIELD value \n", - "UNWIND value['results']['bindings'] as row \n", - "SET r.gender = row['gender']['value'], \n", - " r.manner_of_death = row['death']['value'] \n", - "// Execute FOREACH statement \n", - "FOREACH(ignoreme in case when row['country'] is not null then [1] else [] end | \n", - " MERGE (c:Country{name:row['country']['value']}) \n", - " MERGE (r)-[:IN_COUNTRY]->(c))\n", - "\n", - "\"\"\"\n", - "\n", - "r = run_query(import_gender_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GIpG7UxcHa4P" - }, - "source": [ - "We are connecting additional information to our graph bit by bit and slowly transforming it into a knowledge graph. Let's first look at the manner of death property." + "text/plain": [ + " race members\n", + "0 Middle-earth man 354\n", + "1 Hobbit 150\n", + "2 Middle-earth elf 86\n", + "3 Middle-earth dwarf 52\n", + "4 Valar 16\n", + "5 half-elven 11\n", + "6 Maiar 10\n", + "7 Orcs in Tolkien's legendarium 9\n", + "8 Ent 5\n", + "9 dragons of Middle-earth 3" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "race_size_query = \"\"\"\n", + "\n", + "MATCH (r:Race) \n", + "RETURN r.race as race, \n", + " count{ (r)<-[:BELONG_TO]-() } as members \n", + "ORDER BY members DESC \n", + "LIMIT 10\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(race_size_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UazugeKjHa4N" + }, + "source": [ + "The Fellowship of the Ring group is a somewhat representative sample of races in the Middle-earth. Most of the characters are either human or hobbits, with a couple of elves and dwarves strolling by. This is the first time I have heard of Valar and Maiar races though.\n", + "\n", + "Now it is time to enrich the graph with information about characters' gender, country, and manner of death. The SPARQL query will be a bit different than before. This time we will select a WikiData entity directly by its unique id and optionally fetch some of its properties. We can filter a specific entity by its id using the following SPARQL clause:\n", + "\n", + "filter (?item = wd:' + r.id + ')\n", + "\n", + "Similar to the cypher query language, SPARQL also differentiates between a MATCH and an OPTIONAL MATCH. When we want to return multiple properties of an entity, it is best to wrap each property into an OPTIONAL MATCH. This way we will get results if any of the properties exist. Without the OPTIONAL MATCH, we would only get results for entities where all three properties exist. This is an identical behavior to cypher.\n", + "\n", + "OPTIONAL{ ?item wdt:P21 [rdfs:label ?gender] . \n", + " filter (lang(?gender)=\"en\") }\n", + "\n", + "The wdt:P21 indicates we are interested in the gender property.  We also specify that we want to get the English label of an entity instead of its WikiData id. The easiest way to search for the desired property id is to inspect the entity on the WikiData web page and hover over a property name.\n", + "\n", + "Another way is to use the WikiData query editor, which has a great autocomplete function by using the CTRL+T command.\n", + "\n", + "To store the results back to Neo4j we will use the FOREACH trick. Because some of our results will contain null values, we have to wrap the MERGE statement into the FOREACH statement which supports conditional execution. Check the Tips and tricks blog post by Michael Hunger for more information." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "l3lj59OdHa4O" + }, + "outputs": [], + "source": [ + "import_gender_query = \"\"\"\n", + "\n", + "// Iterate over characters \n", + "MATCH (r:Character) \n", + "// Prepare a SparQL query \n", + "WITH 'SELECT * WHERE{ ?item rdfs:label ?name . filter (?item = wd:' + r.id + ') filter (lang(?name) = \"en\" ) . ' +\n", + " 'OPTIONAL{ ?item wdt:P21 [rdfs:label ?gender] . filter (lang(?gender)=\"en\") } ' + \n", + " 'OPTIONAL{ ?item wdt:P27 [rdfs:label ?country] . filter (lang(?country)=\"en\") } ' +\n", + " 'OPTIONAL{ ?item wdt:P1196 [rdfs:label ?death] . filter (lang(?death)=\"en\") }}' AS sparql, r \n", + "// make a request to Wikidata \n", + "CALL apoc.load.jsonParams( \"https://query.wikidata.org/sparql?query=\" \n", + " + apoc.text.urlencode(sparql), \n", + " { Accept: \"application/sparql-results+json\"}, null)\n", + "YIELD value \n", + "UNWIND value['results']['bindings'] as row \n", + "SET r.gender = row['gender']['value'], \n", + " r.manner_of_death = row['death']['value'] \n", + "// Execute FOREACH statement \n", + "FOREACH(ignoreme in case when row['country'] is not null then [1] else [] end | \n", + " MERGE (c:Country{name:row['country']['value']}) \n", + " MERGE (r)-[:IN_COUNTRY]->(c))\n", + "\n", + "\"\"\"\n", + "\n", + "r = run_query(import_gender_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GIpG7UxcHa4P" + }, + "source": [ + "We are connecting additional information to our graph bit by bit and slowly transforming it into a knowledge graph. Let's first look at the manner of death property." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 }, + "id": "f5s0jWxQHa4P", + "outputId": "4a7b9491-6932-4644-ba34-b0413c21bcf4" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "f5s0jWxQHa4P", - "outputId": "4a7b9491-6932-4644-ba34-b0413c21bcf4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 143 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " manner_of_death count\n", - "0 homicide 3\n", - "1 death in battle 1\n", - "2 accident 1" - ], - "text/html": [ - "\n", - "
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manner_of_deathcount
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2accident1
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\n", + " " ], - "source": [ - "manner_of_death_query = \"\"\"\n", - "\n", - "MATCH (n:Character) \n", - "WHERE exists (n.manner_of_death) \n", - "RETURN n.manner_of_death as manner_of_death, \n", - " count(*) as count\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(manner_of_death_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vBKeyjrFHa4P" - }, - "source": [ - "Nothing of interest. This is obviously not the Game of Thrones series. Let's also inspect the results of the country property." + "text/plain": [ + " manner_of_death count\n", + "0 homicide 3\n", + "1 death in battle 1\n", + "2 accident 1" ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "manner_of_death_query = \"\"\"\n", + "\n", + "MATCH (n:Character) \n", + "WHERE n.manner_of_death IS NOT NULL \n", + "RETURN n.manner_of_death as manner_of_death, \n", + " count(*) as count\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(manner_of_death_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vBKeyjrFHa4P" + }, + "source": [ + "Nothing of interest. This is obviously not the Game of Thrones series. Let's also inspect the results of the country property." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 }, + "id": "H-OtGtHLHa4Q", + "outputId": "ca396601-a14c-4cb6-def0-4ba1769eb837" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "id": "H-OtGtHLHa4Q", - "outputId": "ca396601-a14c-4cb6-def0-4ba1769eb837", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " country members\n", - "0 Gondor 70\n", - "1 Shire 48\n", - "2 Rohan 34\n", - "3 Númenor 34\n", - "4 Arthedain 16\n", - "5 Arnor 8\n", - "6 Doriath 5\n", - "7 Reunited Kingdom 3\n", - "8 Lothlórien 3\n", - "9 Gondolin 3" - ], - "text/html": [ - "\n", - "
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countrymembers
0Gondor70
1Shire48
2Rohan34
3Númenor34
4Arthedain16
5Arnor8
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7Reunited Kingdom3
8Lothlórien3
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countrymembers
0Gondor70
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3Númenor34
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5Arnor8
6Doriath5
7Reunited Kingdom3
8Lothlórien3
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" ], - "source": [ - "country_info_query = \"\"\"\n", - "\n", - "MATCH (c:Country)\n", - "RETURN c.name as country, \n", - " size((c)<-[:IN_COUNTRY]-()) as members\n", - "ORDER BY members DESC \n", - "LIMIT 10\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(country_info_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Bvplf6CEHa4Q" - }, - "source": [ - "We have the country information for 236 characters. We could make some hypotheses and try to populate missing country values. Let's assume that if two characters are siblings, they belong to the same country. This makes a lot of sense. To be able to achieve this, we have to import the familial ties from WikiData. Specifically, we will fetch the father, mother, relative, sibling, and spouse connections." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "id": "BIqDirK8Ha4Q" - }, - "outputs": [], - "source": [ - "import_social_query = \"\"\"\n", - "\n", - "// Iterate over characters \n", - "MATCH (r:Character) \n", - "WITH 'SELECT * WHERE{ ?item rdfs:label ?name . filter (?item = wd:' + r.id + ') filter (lang(?name) = \"en\" ) . ' + \n", - " 'OPTIONAL{ ?item wdt:P22 ?father } OPTIONAL{ ?item wdt:P25 ?mother } OPTIONAL{ ?item wdt:P1038 ?relative } ' +\n", - " 'OPTIONAL{ ?item wdt:P3373 ?sibling } OPTIONAL{ ?item wdt:P26 ?spouse }}' AS sparql, r \n", - "// make a request to wikidata \n", - "CALL apoc.load.jsonParams( \"https://query.wikidata.org/sparql?query=\" + \n", - " sparql, \n", - " { Accept: \"application/sparql-results+json\"}, null) YIELD value \n", - "UNWIND value['results']['bindings'] as row \n", - "FOREACH(ignoreme in case when row['mother'] is not null then [1] else [] end | \n", - " MERGE (c:Character{url:row['mother']['value']}) \n", - " MERGE (r)-[:HAS_MOTHER]->(c)) \n", - "FOREACH(ignoreme in case when row['father'] is not null then [1] else [] end | \n", - " MERGE (c:Character{url:row['father']['value']}) \n", - " MERGE (r)-[:HAS_FATHER]->(c)) \n", - "FOREACH(ignoreme in case when row['relative'] is not null then [1] else [] end | \n", - " MERGE (c:Character{url:row['relative']['value']}) \n", - " MERGE (r)-[:HAS_RELATIVE]-(c)) \n", - "FOREACH(ignoreme in case when row['sibling'] is not null then [1] else [] end | \n", - " MERGE (c:Character{url:row['sibling']['value']}) \n", - " MERGE (r)-[:SIBLING]-(c))\n", - "FOREACH(ignoreme in case when row['spouse'] is not null then [1] else [] end | \n", - " MERGE (c:Character{url:row['spouse']['value']}) \n", - " MERGE (r)-[:SPOUSE]-(c))\n", - "\n", - "\"\"\"\n", - "\n", - "r = run_query(import_social_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "I0qIuaSYHa4R" - }, - "source": [ - "Before we begin filling-in missing values, let's check for promiscuity in the Middle-earth. The first query will search for characters with multiple spouses." + "text/plain": [ + " country members\n", + "0 Gondor 70\n", + "1 Shire 48\n", + "2 Rohan 34\n", + "3 Númenor 34\n", + "4 Arthedain 16\n", + "5 Arnor 8\n", + "6 Doriath 5\n", + "7 Reunited Kingdom 3\n", + "8 Lothlórien 3\n", + "9 Gondolin 3" ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_info_query = \"\"\"\n", + "\n", + "MATCH (c:Country)\n", + "RETURN c.name as country, \n", + " count{ (c)<-[:IN_COUNTRY]-() } as members\n", + "ORDER BY members DESC \n", + "LIMIT 10\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(country_info_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bvplf6CEHa4Q" + }, + "source": [ + "We have the country information for 236 characters. We could make some hypotheses and try to populate missing country values. Let's assume that if two characters are siblings, they belong to the same country. This makes a lot of sense. To be able to achieve this, we have to import the familial ties from WikiData. Specifically, we will fetch the father, mother, relative, sibling, and spouse connections." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "BIqDirK8Ha4Q" + }, + "outputs": [], + "source": [ + "import_social_query = \"\"\"\n", + "\n", + "// Iterate over characters \n", + "MATCH (r:Character) \n", + "WITH 'SELECT * WHERE{ ?item rdfs:label ?name . filter (?item = wd:' + r.id + ') filter (lang(?name) = \"en\" ) . ' + \n", + " 'OPTIONAL{ ?item wdt:P22 ?father } OPTIONAL{ ?item wdt:P25 ?mother } OPTIONAL{ ?item wdt:P1038 ?relative } ' +\n", + " 'OPTIONAL{ ?item wdt:P3373 ?sibling } OPTIONAL{ ?item wdt:P26 ?spouse }}' AS sparql, r \n", + "// make a request to wikidata \n", + "CALL apoc.load.jsonParams( \"https://query.wikidata.org/sparql?query=\" + \n", + " apoc.text.urlencode(sparql), \n", + " { Accept: \"application/sparql-results+json\"}, null) YIELD value \n", + "UNWIND value['results']['bindings'] as row \n", + "FOREACH(ignoreme in case when row['mother'] is not null then [1] else [] end | \n", + " MERGE (c:Character{url:row['mother']['value']}) \n", + " MERGE (r)-[:HAS_MOTHER]->(c)) \n", + "FOREACH(ignoreme in case when row['father'] is not null then [1] else [] end | \n", + " MERGE (c:Character{url:row['father']['value']}) \n", + " MERGE (r)-[:HAS_FATHER]->(c)) \n", + "FOREACH(ignoreme in case when row['relative'] is not null then [1] else [] end | \n", + " MERGE (c:Character{url:row['relative']['value']}) \n", + " MERGE (r)-[:HAS_RELATIVE]-(c)) \n", + "FOREACH(ignoreme in case when row['sibling'] is not null then [1] else [] end | \n", + " MERGE (c:Character{url:row['sibling']['value']}) \n", + " MERGE (r)-[:SIBLING]-(c))\n", + "FOREACH(ignoreme in case when row['spouse'] is not null then [1] else [] end | \n", + " MERGE (c:Character{url:row['spouse']['value']}) \n", + " MERGE (r)-[:SPOUSE]-(c))\n", + "\n", + "\"\"\"\n", + "\n", + "r = run_query(import_social_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I0qIuaSYHa4R" + }, + "source": [ + "Before we begin filling-in missing values, let's check for promiscuity in the Middle-earth. The first query will search for characters with multiple spouses." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 }, + "id": "imDtdPM7Ha4R", + "outputId": "1d605d05-14e1-483b-b115-8d2040bc7ae6" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "id": "imDtdPM7Ha4R", - "outputId": "1d605d05-14e1-483b-b115-8d2040bc7ae6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " result\n", - "0 [Indis, Finwë, Míriel]\n", - "1 [Míriel, Finwë, Indis]" - ], - "text/html": [ - "\n", - "
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result
0[Indis, Finwë, Míriel]
1[Míriel, Finwë, Indis]
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result
0[Indis, Finwë, Míriel]
1[Míriel, Finwë, Indis]
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" ], - "source": [ - "multiple_spouses_query = \"\"\"\n", - "\n", - "MATCH p=(a)-[:SPOUSE]-(b)-[:SPOUSE]-(c) \n", - "RETURN [n IN nodes(p) | n.name] AS result LIMIT 10\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(multiple_spouses_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NdC3zUP0Ha4R" - }, - "source": [ - "We actually found a single character with two spouses. It is Finwë, the first King of the Noldor. We can also take a look if someone has kids with multiple partners" + "text/plain": [ + " result\n", + "0 [Indis, Finwë, Míriel]\n", + "1 [Míriel, Finwë, Indis]" ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "multiple_spouses_query = \"\"\"\n", + "\n", + "MATCH p=(a)-[:SPOUSE]-(b)-[:SPOUSE]-(c) \n", + "RETURN [n IN nodes(p) | n.name] AS result LIMIT 10\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(multiple_spouses_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NdC3zUP0Ha4R" + }, + "source": [ + "We actually found a single character with two spouses. It is Finwë, the first King of the Noldor. We can also take a look if someone has kids with multiple partners" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 }, + "id": "AFYIBdk8Ha4S", + "outputId": "b20d060a-9dc0-4fad-c808-387ea40f5151" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "id": "AFYIBdk8Ha4S", - "outputId": "b20d060a-9dc0-4fad-c808-387ea40f5151", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " result\n", - "0 [Finwë, Fingolfin, Indis]\n", - "1 [Finwë, Finarfin, Indis]\n", - "2 [Finwë, Findis, Indis]\n", - "3 [Finwë, Irimë, Indis]\n", - "4 [Finwë, Fëanor, Míriel]" - ], - "text/html": [ - "\n", - "
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result
0[Finwë, Fingolfin, Indis]
1[Finwë, Finarfin, Indis]
2[Finwë, Findis, Indis]
3[Finwë, Irimë, Indis]
4[Finwë, Fëanor, Míriel]
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result
0[Finwë, Fingolfin, Indis]
1[Finwë, Findis, Indis]
2[Finwë, Irimë, Indis]
3[Finwë, Finarfin, Indis]
4[Finwë, Fëanor, Míriel]
\n", + "
" ], - "source": [ - "multiple_kids_query = \"\"\"\n", - "\n", - "MATCH (c:Character)<-[:HAS_FATHER|HAS_MOTHER]-()-[:HAS_FATHER|HAS_MOTHER]->(other) \n", - "WITH c, collect(distinct other) as others \n", - "WHERE size(others) > 1 \n", - "MATCH p=(c)<-[:HAS_FATHER|HAS_MOTHER]-()-[:HAS_FATHER|HAS_MOTHER]->() \n", - "RETURN [n IN nodes(p) | n.name] AS result LIMIT 10\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(multiple_kids_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YcfTme1qHa4S" - }, - "source": [ - "So it seems that Finwë has four children with Indis and a single child with Míriel. On the other hand, it is quite weird that Beren has two fathers. I guess Adanel has some explaining to do. We would probably find more death and promiscuity in the GoT world.\n", - "\n", - "## Populate missing values\n", - "\n", - "Now that we know that the Middle-earth characters abstain from promiscuity, let's populate the missing country values. Remember our hypothesis was:\n", - "\n", - ">If two characters are siblings, they belong to the same country.\n", - "\n", - "Before we populate the missing values for countries, let's populate the missing values for siblings. We will assume that if two characters have the same mother or father, they are siblings. Let's look at some sibling candidates." + "text/plain": [ + " result\n", + "0 [Finwë, Fingolfin, Indis]\n", + "1 [Finwë, Findis, Indis]\n", + "2 [Finwë, Irimë, Indis]\n", + "3 [Finwë, Finarfin, Indis]\n", + "4 [Finwë, Fëanor, Míriel]" ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "multiple_kids_query = \"\"\"\n", + "\n", + "MATCH (c:Character)<-[:HAS_FATHER|HAS_MOTHER]-()-[:HAS_FATHER|HAS_MOTHER]->(other) \n", + "WITH c, collect(distinct other) as others \n", + "WHERE size(others) > 1 \n", + "MATCH p=(c)<-[:HAS_FATHER|HAS_MOTHER]-()-[:HAS_FATHER|HAS_MOTHER]->() \n", + "RETURN [n IN nodes(p) | n.name] AS result LIMIT 10\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(multiple_kids_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YcfTme1qHa4S" + }, + "source": [ + "So it seems that Finwë has four children with Indis and a single child with Míriel. On the other hand, it is quite weird that Beren has two fathers. I guess Adanel has some explaining to do. We would probably find more death and promiscuity in the GoT world.\n", + "\n", + "## Populate missing values\n", + "\n", + "Now that we know that the Middle-earth characters abstain from promiscuity, let's populate the missing country values. Remember our hypothesis was:\n", + "\n", + ">If two characters are siblings, they belong to the same country.\n", + "\n", + "Before we populate the missing values for countries, let's populate the missing values for siblings. We will assume that if two characters have the same mother or father, they are siblings. Let's look at some sibling candidates." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 }, + "id": "XWdkChq-Ha4T", + "outputId": "7e511e96-9487-4c73-b69e-9e85dca43890" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "id": "XWdkChq-Ha4T", - "outputId": "7e511e96-9487-4c73-b69e-9e85dca43890", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " result\n", - "0 [Ferumbras Took II, Isumbras Took III, Bandobr...\n", - "1 [Bingo Baggins, Laura Grubb, Bungo Baggins]\n", - "2 [Belba Baggins, Laura Grubb, Bungo Baggins]\n", - "3 [Linda Proudfoot, Laura Grubb, Bungo Baggins]\n", - "4 [Bingo Baggins, Mungo Baggins, Bungo Baggins]\n", - "5 [Linda Proudfoot, Mungo Baggins, Bungo Baggins]\n", - "6 [Belba Baggins, Mungo Baggins, Bungo Baggins]\n", - "7 [Hildigard Took, Gerontius Took, Isembard Took]\n", - "8 [Isengar Took, Gerontius Took, Isembard Took]\n", - "9 [Isengrim Took III, Gerontius Took, Isembard T..." - ], - "text/html": [ - "\n", - "
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0[Ferumbras Took II, Isumbras Took III, Bandobr...
1[Bingo Baggins, Laura Grubb, Bungo Baggins]
2[Belba Baggins, Laura Grubb, Bungo Baggins]
3[Linda Proudfoot, Laura Grubb, Bungo Baggins]
4[Bingo Baggins, Mungo Baggins, Bungo Baggins]
5[Linda Proudfoot, Mungo Baggins, Bungo Baggins]
6[Belba Baggins, Mungo Baggins, Bungo Baggins]
7[Hildigard Took, Gerontius Took, Isembard Took]
8[Isengar Took, Gerontius Took, Isembard Took]
9[Isengrim Took III, Gerontius Took, Isembard T...
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result
0[Ferumbras Took II, Isumbras Took III, Bandobr...
1[Linda Proudfoot, Mungo Baggins, Bungo Baggins]
2[Bingo Baggins, Mungo Baggins, Bungo Baggins]
3[Belba Baggins, Mungo Baggins, Bungo Baggins]
4[Belba Baggins, Laura Grubb, Bungo Baggins]
5[Linda Proudfoot, Laura Grubb, Bungo Baggins]
6[Bingo Baggins, Laura Grubb, Bungo Baggins]
7[Isembold Took, Adamanta Chubb, Isembard Took]
8[Isengar Took, Adamanta Chubb, Isembard Took]
9[Donnamira Took, Adamanta Chubb, Isembard Took]
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" ], - "source": [ - "sibling_candidate_query = \"\"\"\n", - "\n", - "MATCH p=(a:Character)-[:HAS_FATHER|:HAS_MOTHER]->()<-[:HAS_FATHER|:HAS_MOTHER]-(b:Character) \n", - "WHERE NOT (a)-[:SIBLING]-(b) \n", - "RETURN [n IN nodes(p) | n.name] AS result LIMIT 10\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(sibling_candidate_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bHKPYakKHa4T" - }, - "source": [ - "Adamanta Chubb has at least six children. Only two of them are marked as siblings. Because all of them are siblings by definition, we will fill in the missing connections." + "text/plain": [ + " result\n", + "0 [Ferumbras Took II, Isumbras Took III, Bandobr...\n", + "1 [Linda Proudfoot, Mungo Baggins, Bungo Baggins]\n", + "2 [Bingo Baggins, Mungo Baggins, Bungo Baggins]\n", + "3 [Belba Baggins, Mungo Baggins, Bungo Baggins]\n", + "4 [Belba Baggins, Laura Grubb, Bungo Baggins]\n", + "5 [Linda Proudfoot, Laura Grubb, Bungo Baggins]\n", + "6 [Bingo Baggins, Laura Grubb, Bungo Baggins]\n", + "7 [Isembold Took, Adamanta Chubb, Isembard Took]\n", + "8 [Isengar Took, Adamanta Chubb, Isembard Took]\n", + "9 [Donnamira Took, Adamanta Chubb, Isembard Took]" ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sibling_candidate_query = \"\"\"\n", + "\n", + "MATCH p=(a:Character)-[:HAS_FATHER|:HAS_MOTHER]->()<-[:HAS_FATHER|:HAS_MOTHER]-(b:Character) \n", + "WHERE NOT exists { (a)-[:SIBLING]-(b) } \n", + "RETURN [n IN nodes(p) | n.name] AS result LIMIT 10\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(sibling_candidate_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bHKPYakKHa4T" + }, + "source": [ + "Adamanta Chubb has at least six children. Only two of them are marked as siblings. Because all of them are siblings by definition, we will fill in the missing connections." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49 }, + "id": "VHdXvvIHHa4T", + "outputId": "f3095cf0-4496-41d5-eb6f-7a9e52d7ffb4" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "id": "VHdXvvIHHa4T", - "outputId": "f3095cf0-4496-41d5-eb6f-7a9e52d7ffb4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: []" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "sibling_populate_query = \"\"\"\n", - "\n", - "MATCH p=(a:Character)-[:HAS_FATHER|:HAS_MOTHER]->()<-[:HAS_FATHER|:HAS_MOTHER]-(b:Character) \n", - "WHERE NOT (a)-[:SIBLING]-(b) \n", - "MERGE (a)-[:SIBLING]-(b)\n", - "\n", - "\"\"\"\n", - "run_query(sibling_populate_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LsxV7gZJHa4T" - }, - "source": [ - "The query added 118 missing relationships. I need to learn how to update the WikiData knowledge graph and add the missing relationships in bulk. Now we can fill in the missing country values for siblings. We will match all characters with the filled in country information and search for their siblings that don't have the country information. I love how easy it is to express this pattern with cypher query language." + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sibling_populate_query = \"\"\"\n", + "\n", + "MATCH p=(a:Character)-[:HAS_FATHER|:HAS_MOTHER]->()<-[:HAS_FATHER|:HAS_MOTHER]-(b:Character) \n", + "WHERE NOT exists { (a)-[:SIBLING]-(b) } \n", + "MERGE (a)-[:SIBLING]-(b)\n", + "\n", + "\"\"\"\n", + "run_query(sibling_populate_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LsxV7gZJHa4T" + }, + "source": [ + "The query added 118 missing relationships. I need to learn how to update the WikiData knowledge graph and add the missing relationships in bulk. Now we can fill in the missing country values for siblings. We will match all characters with the filled in country information and search for their siblings that don't have the country information. I love how easy it is to express this pattern with cypher query language." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49 }, + "id": "Ipj0d95THa4T", + "outputId": "2d95cf28-d60a-487d-d51e-a8ce2a327ef6" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "id": "Ipj0d95THa4T", - "outputId": "2d95cf28-d60a-487d-d51e-a8ce2a327ef6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: []" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "country_populate_query = \"\"\"\n", - "\n", - "MATCH (country)<-[:IN_COUNTRY]-(s:Character)-[:SIBLING]-(t:Character) \n", - "WHERE NOT (t)-[:IN_COUNTRY]->() \n", - "MERGE (t)-[:IN_COUNTRY]->(country)\n", - "\n", - "\"\"\"\n", - "run_query(country_populate_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KDh1CjK3Ha4U" - }, - "source": [ - "There were 49 missing countries added. We could easily come up with more hypotheses to fill in the missing values. You can try and maybe add some other missing values yourself.\n", - "\n", - "We still have to add some information to our graph. In this query, we will add the information about the occupation, language, groups, and events of characters. The SPARQL query is identical to before where we iterate over each character and fetch additional properties." + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_populate_query = \"\"\"\n", + "\n", + "MATCH (country)<-[:IN_COUNTRY]-(s:Character)-[:SIBLING]-(t:Character) \n", + "WHERE NOT exists { (t)-[:IN_COUNTRY]->() }\n", + "MERGE (t)-[:IN_COUNTRY]->(country)\n", + "\n", + "\"\"\"\n", + "run_query(country_populate_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KDh1CjK3Ha4U" + }, + "source": [ + "There were 49 missing countries added. We could easily come up with more hypotheses to fill in the missing values. You can try and maybe add some other missing values yourself.\n", + "\n", + "We still have to add some information to our graph. In this query, we will add the information about the occupation, language, groups, and events of characters. The SPARQL query is identical to before where we iterate over each character and fetch additional properties." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49 }, + "id": "3AVPww1fHa4U", + "outputId": "faae2f85-abe9-4d2e-a795-9921abe21ce2" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "id": "3AVPww1fHa4U", - "outputId": "faae2f85-abe9-4d2e-a795-9921abe21ce2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: []" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "import_groups_query = \"\"\"\n", - "\n", - "MATCH (r:Character) \n", - "WHERE exists (r.id) \n", - "WITH 'SELECT * WHERE{ ?item rdfs:label ?name . filter (?item = wd:' + r.id + ') filter (lang(?name) = \"en\" ) . ' +\n", - " 'OPTIONAL { ?item wdt:P106 [rdfs:label ?occupation ] . filter (lang(?occupation) = \"en\" ). } ' +\n", - " 'OPTIONAL { ?item wdt:P103 [rdfs:label ?language ] . filter (lang(?language) = \"en\" ) . } ' +\n", - " 'OPTIONAL { ?item wdt:P463 [rdfs:label ?member_of ] . filter (lang(?member_of) = \"en\" ). } ' +\n", - " 'OPTIONAL { ?item wdt:P1344[rdfs:label ?participant ] . filter (lang(?participant) = \"en\") . } ' +\n", - " 'OPTIONAL { ?item wdt:P39[rdfs:label ?position ] . filter (lang(?position) = \"en\") . }}' AS sparql, r \n", - "CALL apoc.load.jsonParams( \"https://query.wikidata.org/sparql?query=\" + \n", - " sparql, \n", - " { Accept: \"application/sparql-results+json\"}, null) \n", - "YIELD value \n", - "UNWIND value['results']['bindings'] as row \n", - "FOREACH(ignoreme in case when row['language'] is not null then [1] else [] end | \n", - " MERGE (c:Language{name:row['language']['value']}) \n", - " MERGE (r)-[:HAS_LANGUAGE]->(c)) \n", - "FOREACH(ignoreme in case when row['occupation'] is not null then [1] else [] end | \n", - " MERGE (c:Occupation{name:row['occupation']['value']}) \n", - " MERGE (r)-[:HAS_OCCUPATION]->(c)) \n", - "FOREACH(ignoreme in case when row['member_of'] is not null then [1] else [] end | \n", - " MERGE (c:Group{name:row['member_of']['value']}) \n", - " MERGE (r)-[:MEMBER_OF]->(c)) \n", - "FOREACH(ignoreme in case when row['participant'] is not null then [1] else [] end | \n", - " MERGE (c:Event{name:row['participant']['value']}) \n", - " MERGE (r)-[:PARTICIPATED]->(c)) \n", - "SET r.position = row['position']['value']\n", - "\n", - "\"\"\"\n", - "run_query(import_groups_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OVN0slGqHa4U" - }, - "source": [ - "Let's investigate the results of the groups and the occupation of the characters." + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import_groups_query = \"\"\"\n", + "\n", + "MATCH (r:Character) \n", + "WHERE r.id IS NOT NULL \n", + "WITH 'SELECT * WHERE{ ?item rdfs:label ?name . filter (?item = wd:' + r.id + ') filter (lang(?name) = \"en\" ) . ' +\n", + " 'OPTIONAL { ?item wdt:P106 [rdfs:label ?occupation ] . filter (lang(?occupation) = \"en\" ). } ' +\n", + " 'OPTIONAL { ?item wdt:P103 [rdfs:label ?language ] . filter (lang(?language) = \"en\" ) . } ' +\n", + " 'OPTIONAL { ?item wdt:P463 [rdfs:label ?member_of ] . filter (lang(?member_of) = \"en\" ). } ' +\n", + " 'OPTIONAL { ?item wdt:P1344[rdfs:label ?participant ] . filter (lang(?participant) = \"en\") . } ' +\n", + " 'OPTIONAL { ?item wdt:P39[rdfs:label ?position ] . filter (lang(?position) = \"en\") . }}' AS sparql, r \n", + "CALL apoc.load.jsonParams( \"https://query.wikidata.org/sparql?query=\" + \n", + " apoc.text.urlencode(sparql), \n", + " { Accept: \"application/sparql-results+json\"}, null) \n", + "YIELD value \n", + "UNWIND value['results']['bindings'] as row \n", + "FOREACH(ignoreme in case when row['language'] is not null then [1] else [] end | \n", + " MERGE (c:Language{name:row['language']['value']}) \n", + " MERGE (r)-[:HAS_LANGUAGE]->(c)) \n", + "FOREACH(ignoreme in case when row['occupation'] is not null then [1] else [] end | \n", + " MERGE (c:Occupation{name:row['occupation']['value']}) \n", + " MERGE (r)-[:HAS_OCCUPATION]->(c)) \n", + "FOREACH(ignoreme in case when row['member_of'] is not null then [1] else [] end | \n", + " MERGE (c:Group{name:row['member_of']['value']}) \n", + " MERGE (r)-[:MEMBER_OF]->(c)) \n", + "FOREACH(ignoreme in case when row['participant'] is not null then [1] else [] end | \n", + " MERGE (c:Event{name:row['participant']['value']}) \n", + " MERGE (r)-[:PARTICIPATED]->(c)) \n", + "SET r.position = row['position']['value']\n", + "\n", + "\"\"\"\n", + "run_query(import_groups_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OVN0slGqHa4U" + }, + "source": [ + "Let's investigate the results of the groups and the occupation of the characters." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 }, + "id": "NQsHXDmgHa4V", + "outputId": "fefc55bc-65c1-4a95-c4ce-12afc8c53f12" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "id": "NQsHXDmgHa4V", - "outputId": "fefc55bc-65c1-4a95-c4ce-12afc8c53f12", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 300 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " group size members \\\n", - "0 Thorin and Company 14 [Bofur, Óin, Glóin] \n", - "1 Fellowship of the Ring 8 [Samwise Gamgee, Frodo Baggins, Legolas] \n", - "2 White Council 2 [Elrond, Gandalf] \n", - "3 Union of Maedhros 2 [Haldir, Halmir] \n", - "4 Wise 2 [Adanel, Andreth] \n", - "5 Rangers of Ithilien 2 [Damrod, Madril] \n", - "6 Istari 1 [Gandalf] \n", - "7 White Company 1 [Beregond] \n", - "\n", - " occupations \n", - "0 [diarist, swordfighter] \n", - "1 [swordfighter, archer] \n", - "2 [] \n", - "3 [] \n", - "4 [] \n", - "5 [] \n", - "6 [] \n", - "7 [] " - ], - "text/html": [ - "\n", - "
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groupsizemembersoccupations
0Thorin and Company14[Bofur, Óin, Glóin][diarist, swordfighter]
1Fellowship of the Ring8[Samwise Gamgee, Frodo Baggins, Legolas][swordfighter, archer]
2White Council2[Elrond, Gandalf][]
3Union of Maedhros2[Haldir, Halmir][]
4Wise2[Adanel, Andreth][]
5Rangers of Ithilien2[Damrod, Madril][]
6Istari1[Gandalf][]
7White Company1[Beregond][]
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groupsizemembersoccupations
0Thorin and Company14[Glóin, Bofur, Thorin II][swordfighter, diarist]
1Fellowship of the Ring12[Gimli, Peregrin Took, Samwise Gamgee][swordfighter, domestic worker, gardener]
2White Council3[Elrond, Gandalf, Gandalf][magician, swordfighter]
3Rangers of Ithilien2[Damrod, Madril][]
4Union of Maedhros2[Haldir, Halmir][]
5Wise2[Adanel, Andreth][]
6Istari2[Gandalf, Gandalf][magician, swordfighter]
7White Company1[Beregond][guard]
\n", + "
" ], - "source": [ - "investigate_groups_query = \"\"\"\n", - "\n", - "MATCH (n:Group)<-[:MEMBER_OF]-(c)\n", - "OPTIONAL MATCH (c)-[:HAS_OCCUPATION]->(o) \n", - "RETURN n.name as group, \n", - " count(*) as size, \n", - " collect(c.name)[..3] as members, \n", - " collect(distinct o.name)[..3] as occupations \n", - "ORDER BY size DESC\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(investigate_groups_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nZGm5a1VHa4V" - }, - "source": [ - "It was at this moment that I realized the whole Hobbit series are included. Balin was the diarist for the Thorin and Company group. For some reason, I was expecting Bilbo Baggins to be the diarist. Obviously, there can be only one archer in the Fellowship of the Ring group, and that is Legolas. Gandalf seems to be involved in a couple of groups.\n", - "\n", - "We will execute one more WikiData API call. This time we will fetch the enemies and the items the characters own." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "id": "iZ075XIuHa4V" - }, - "outputs": [], - "source": [ - "import_enemy_query = \"\"\"\n", - "\n", - "MATCH (r:Character) \n", - "WHERE exists (r.id) \n", - "WITH 'SELECT * WHERE { ?item rdfs:label ?name . filter (?item = wd:' + r.id + ') filter (lang(?name) = \"en\" ) . ' +\n", - " 'OPTIONAL{ ?item wdt:P1830 [rdfs:label ?owner ] . filter (lang(?owner) = \"en\" ). } ' +\n", - " 'OPTIONAL{ ?item wdt:P7047 ?enemy }}' AS sparql, r \n", - "CALL apoc.load.jsonParams( \"https://query.wikidata.org/sparql?query=\" + \n", - " sparql, \n", - " { Accept: \"application/sparql-results+json\"}, null) \n", - "YIELD value \n", - "WITH value,r \n", - "WHERE value['results']['bindings'] <> [] \n", - "UNWIND value['results']['bindings'] as row \n", - "FOREACH(ignoreme in case when row['owner'] is not null then [1] else [] end |\n", - " MERGE (c:Item{name:row['owner']['value']}) \n", - " MERGE (r)-[:OWNS_ITEM]->(c)) \n", - "FOREACH(ignoreme in case when row['enemy'] is not null then [1] else [] end | \n", - " MERGE (c:Character{url:row['enemy']['value']}) \n", - " MERGE (r)-[:ENEMY]->(c))\n", - "\n", - "\"\"\"\n", - "\n", - "r = execute_query(import_enemy_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IGRptZDwHa4W" - }, - "source": [ - "Finally, we have finished importing our graph. Let's look at how many enemies are there between direct family members." + "text/plain": [ + " group size members \\\n", + "0 Thorin and Company 14 [Glóin, Bofur, Thorin II] \n", + "1 Fellowship of the Ring 12 [Gimli, Peregrin Took, Samwise Gamgee] \n", + "2 White Council 3 [Elrond, Gandalf, Gandalf] \n", + "3 Rangers of Ithilien 2 [Damrod, Madril] \n", + "4 Union of Maedhros 2 [Haldir, Halmir] \n", + "5 Wise 2 [Adanel, Andreth] \n", + "6 Istari 2 [Gandalf, Gandalf] \n", + "7 White Company 1 [Beregond] \n", + "\n", + " occupations \n", + "0 [swordfighter, diarist] \n", + "1 [swordfighter, domestic worker, gardener] \n", + "2 [magician, swordfighter] \n", + "3 [] \n", + "4 [] \n", + "5 [] \n", + "6 [magician, swordfighter] \n", + "7 [guard] " ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "investigate_groups_query = \"\"\"\n", + "\n", + "MATCH (n:Group)<-[:MEMBER_OF]-(c)\n", + "OPTIONAL MATCH (c)-[:HAS_OCCUPATION]->(o) \n", + "RETURN n.name as group, \n", + " count(*) as size, \n", + " collect(c.name)[..3] as members, \n", + " collect(distinct o.name)[..3] as occupations \n", + "ORDER BY size DESC\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(investigate_groups_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nZGm5a1VHa4V" + }, + "source": [ + "It was at this moment that I realized the whole Hobbit series are included. Balin was the diarist for the Thorin and Company group. For some reason, I was expecting Bilbo Baggins to be the diarist. Obviously, there can be only one archer in the Fellowship of the Ring group, and that is Legolas. Gandalf seems to be involved in a couple of groups.\n", + "\n", + "We will execute one more WikiData API call. This time we will fetch the enemies and the items the characters own." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "iZ075XIuHa4V" + }, + "outputs": [], + "source": [ + "import_enemy_query = \"\"\"\n", + "\n", + "MATCH (r:Character) \n", + "WHERE r.id IS NOT NULL \n", + "WITH 'SELECT * WHERE { ?item rdfs:label ?name . filter (?item = wd:' + r.id + ') filter (lang(?name) = \"en\" ) . ' +\n", + " 'OPTIONAL{ ?item wdt:P1830 [rdfs:label ?owner ] . filter (lang(?owner) = \"en\" ). } ' +\n", + " 'OPTIONAL{ ?item wdt:P7047 ?enemy }}' AS sparql, r \n", + "CALL apoc.load.jsonParams( \"https://query.wikidata.org/sparql?query=\" + \n", + " apoc.text.urlencode(sparql), \n", + " { Accept: \"application/sparql-results+json\"}, null) \n", + "YIELD value \n", + "WITH value,r \n", + "WHERE value['results']['bindings'] <> [] \n", + "UNWIND value['results']['bindings'] as row \n", + "FOREACH(ignoreme in case when row['owner'] is not null then [1] else [] end |\n", + " MERGE (c:Item{name:row['owner']['value']}) \n", + " MERGE (r)-[:OWNS_ITEM]->(c)) \n", + "FOREACH(ignoreme in case when row['enemy'] is not null then [1] else [] end | \n", + " MERGE (c:Character{url:row['enemy']['value']}) \n", + " MERGE (r)-[:ENEMY]->(c))\n", + "\n", + "\"\"\"\n", + "\n", + "r = run_query(import_enemy_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IGRptZDwHa4W" + }, + "source": [ + "Finally, we have finished importing our graph. Let's look at how many enemies are there between direct family members." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 }, + "id": "lMs1wi0fHa4W", + "outputId": "2d76e2b2-6d8f-472e-f92d-671a1539be0a" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "id": "lMs1wi0fHa4W", - "outputId": "2d76e2b2-6d8f-472e-f92d-671a1539be0a", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " result\n", - "0 [Manwë, Morgoth]\n", - "1 [Morgoth, Manwë]" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "family_enemy_query = \"\"\"\n", - "\n", - "MATCH p=(a)-[:SPOUSE|SIBLING|HAS_FATHER|HAS_MOTHER]-(b) \n", - "WHERE (a)-[:ENEMY]-(b) \n", - "RETURN [n IN nodes(p) | n.name] AS result LIMIT 10\n", - "\n", - "\"\"\"\n", - "run_query(family_enemy_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "m-s2T9ZIHa4W" - }, - "source": [ - "It looks like Morgoth and Manwë are brothers and enemies. This is the first time I have heard of the two, but LOTR fandom site claims Morgoth was the first Dark Lord. Let's look at how many enemies are within the second-degree relatives." + "text/plain": [ + " result\n", + "0 [Manwë, Morgoth]\n", + "1 [Morgoth, Manwë]" ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "family_enemy_query = \"\"\"\n", + "\n", + "MATCH p=(a)-[:SPOUSE|SIBLING|HAS_FATHER|HAS_MOTHER]-(b) \n", + "WHERE exists { (a)-[:ENEMY]-(b) } \n", + "RETURN [n IN nodes(p) | n.name] AS result LIMIT 10\n", + "\n", + "\"\"\"\n", + "run_query(family_enemy_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m-s2T9ZIHa4W" + }, + "source": [ + "It looks like Morgoth and Manwë are brothers and enemies. This is the first time I have heard of the two, but LOTR fandom site claims Morgoth was the first Dark Lord. Let's look at how many enemies are within the second-degree relatives." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 }, + "id": "MiRNuFtyHa4W", + "outputId": "5e5f5a11-2caf-4d39-e758-1e1bf4fed74c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "id": "MiRNuFtyHa4W", - "outputId": "5e5f5a11-2caf-4d39-e758-1e1bf4fed74c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 175 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " result\n", - "0 [Manwë, Morgoth]\n", - "1 [Morgoth, Manwë]\n", - "2 [Morgoth, Manwë, Varda]\n", - "3 [Varda, Manwë, Morgoth]" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "family_enemy_2hops_query = \"\"\"\n", - "\n", - "MATCH p=(a)-[:SPOUSE|SIBLING|HAS_FATHER|HAS_MOTHER*..2]-(b) \n", - "WHERE (a)-[:ENEMY]-(b) \n", - "RETURN [n IN nodes(p) | n.name] AS result LIMIT 10\n", - "\n", - "\"\"\"\n", - "run_query(family_enemy_2hops_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vNIv7qiMHa4X" - }, - "source": [ - "Not a lot of enemies within the second-degree relatives. We can observe that Varda has taken her husband's stance and is also an enemy with Morgoth. This is an example of a stable triangle or triad. The triangle consists of one positive relationship (SPOUSE) and two negatives (ENEMY). In social network analysis, triangles are used to measure the cohesiveness and structural stability of a network.\n", - "\n", - "## Graph data science\n", - "\n", - "If you have read any of my previous blog posts, you know that I just have to include some example use cases of graph algorithms from the Graph Data Science library. If you need a quick refresher on how the GDS library works and what is happening behind the scenes, I suggest you read my previous blog post.\n", - "\n", - "We will start by projecting the family network. We load all the characters and the familial relationships like SPOUSE, SIBLING, HAS_FATHER, and HAS_MOTHER between them." + "text/plain": [ + " result\n", + "0 [Manwë, Morgoth]\n", + "1 [Morgoth, Manwë]\n", + "2 [Morgoth, Manwë, Varda]\n", + "3 [Varda, Manwë, Morgoth]" ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "family_enemy_2hops_query = \"\"\"\n", + "\n", + "MATCH p=(a)-[:SPOUSE|SIBLING|HAS_FATHER|HAS_MOTHER*..2]-(b) \n", + "WHERE exists { (a)-[:ENEMY]-(b) } \n", + "RETURN [n IN nodes(p) | n.name] AS result LIMIT 10\n", + "\n", + "\"\"\"\n", + "run_query(family_enemy_2hops_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vNIv7qiMHa4X" + }, + "source": [ + "Not a lot of enemies within the second-degree relatives. We can observe that Varda has taken her husband's stance and is also an enemy with Morgoth. This is an example of a stable triangle or triad. The triangle consists of one positive relationship (SPOUSE) and two negatives (ENEMY). In social network analysis, triangles are used to measure the cohesiveness and structural stability of a network.\n", + "\n", + "## Graph data science\n", + "\n", + "If you have read any of my previous blog posts, you know that I just have to include some example use cases of graph algorithms from the Graph Data Science library. If you need a quick refresher on how the GDS library works and what is happening behind the scenes, I suggest you read my previous blog post.\n", + "\n", + "We will start by projecting the family network. We load all the characters and the familial relationships like SPOUSE, SIBLING, HAS_FATHER, and HAS_MOTHER between them." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "ND_zVoYOHa4X", + "outputId": "88bccf60-ff12-43a1-b663-570dfedfa92e" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "id": "ND_zVoYOHa4X", - "outputId": "88bccf60-ff12-43a1-b663-570dfedfa92e", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeProjection \\\n", - "0 {'Character': {'label': 'Character', 'properti... \n", - "\n", - " relationshipProjection graphName nodeCount \\\n", - "0 {'HAS_MOTHER': {'orientation': 'NATURAL', 'agg... family 699 \n", - "\n", - " relationshipCount projectMillis \n", - "0 1054 102 " - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "project_graph = \"\"\"\n", - "CALL gds.graph.project('family','Character', \n", - " ['SPOUSE','SIBLING','HAS_FATHER','HAS_MOTHER'])\n", - "\"\"\"\n", - "run_query(project_graph)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HUBD0xphHa4X" - }, - "source": [ - "### Weakly connected component\n", - "\n", - "The weakly connected component algorithm is used to find islands or disconnected components within our network. The following visualizations contain two connected components. The first component is composed of Michael, Mark, and Doug while the second one consists of Alice, Charles, and Bridget.\n", - "\n", - "In our case, we will use the weakly connected component algorithm to find islands within the family network. All members within the same family component are related to each other somehow. Could be a cousin of the sister-in-law's grandmother or something more direct like a sibling. To get a rough feeling of the results, we will run the stats mode of the algorithm." + "text/plain": [ + " nodeProjection \\\n", + "0 {'Character': {'label': 'Character', 'properti... \n", + "\n", + " relationshipProjection graphName nodeCount \\\n", + "0 {'HAS_MOTHER': {'orientation': 'NATURAL', 'ind... family 710 \n", + "\n", + " relationshipCount projectMillis \n", + "0 1060 26 " ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "project_graph = \"\"\"\n", + "CALL gds.graph.project('family','Character', \n", + " ['SPOUSE','SIBLING','HAS_FATHER','HAS_MOTHER'])\n", + "\"\"\"\n", + "run_query(project_graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HUBD0xphHa4X" + }, + "source": [ + "### Weakly connected component\n", + "\n", + "The weakly connected component algorithm is used to find islands or disconnected components within our network. The following visualizations contain two connected components. The first component is composed of Michael, Mark, and Doug while the second one consists of Alice, Charles, and Bridget.\n", + "\n", + "In our case, we will use the weakly connected component algorithm to find islands within the family network. All members within the same family component are related to each other somehow. Could be a cousin of the sister-in-law's grandmother or something more direct like a sibling. To get a rough feeling of the results, we will run the stats mode of the algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "3g8ufqCcHa4X", + "outputId": "f70b07d5-ad38-49ff-8014-72a4461b3932" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "id": "3g8ufqCcHa4X", - "outputId": "f70b07d5-ad38-49ff-8014-72a4461b3932", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " components p75 p90 mean max\n", - "0 147 1 3 4.76 324" - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "wcc_stats_query = \"\"\"\n", - "\n", - "CALL gds.wcc.stats('family') \n", - "YIELD componentCount, \n", - " componentDistribution \n", - "RETURN componentCount as components, \n", - " componentDistribution.p75 as p75, \n", - " componentDistribution.p90 as p90, \n", - " apoc.math.round(componentDistribution.mean,2) as mean, \n", - " componentDistribution.max as max\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(wcc_stats_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nCgfAjCmHa4X" - }, - "source": [ - "There are 145 connected components in our graph. More than 75% of the components contain only a single character. This means that around 110 (75% * 145) characters don't have a single familial link to any other character. If they had a single link, the size of the component would be at least two.  The biggest component has 328 members, so that must be one happy family. Let's write back the results and further analyze the family components." + "text/plain": [ + " components p75 p90 mean max\n", + "0 156 1 3 4.55 321" ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wcc_stats_query = \"\"\"\n", + "\n", + "CALL gds.wcc.stats('family') \n", + "YIELD componentCount, \n", + " componentDistribution \n", + "RETURN componentCount as components, \n", + " componentDistribution.p75 as p75, \n", + " componentDistribution.p90 as p90, \n", + " round(componentDistribution.mean,2) as mean, \n", + " componentDistribution.max as max\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(wcc_stats_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nCgfAjCmHa4X" + }, + "source": [ + "There are 145 connected components in our graph. More than 75% of the components contain only a single character. This means that around 110 (75% * 145) characters don't have a single familial link to any other character. If they had a single link, the size of the component would be at least two.  The biggest component has 328 members, so that must be one happy family. Let's write back the results and further analyze the family components." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "YDdRNi4EHa4Y", + "outputId": "264f1bb5-1078-45ef-b89b-56d660269a50" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "id": "YDdRNi4EHa4Y", - "outputId": "264f1bb5-1078-45ef-b89b-56d660269a50", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " writeMillis nodePropertiesWritten componentCount \\\n", - "0 181 699 147 \n", - "\n", - " componentDistribution postProcessingMillis \\\n", - "0 {'p99': 139, 'min': 1, 'max': 324, 'mean': 4.7... 5 \n", - "\n", - " preProcessingMillis computeMillis \\\n", - "0 0 19 \n", - "\n", - " configuration \n", - "0 {'writeConcurrency': 4, 'seedProperty': None, ... " - ], - "text/html": [ - "\n", - "
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writeMillisnodePropertiesWrittencomponentCountcomponentDistributionpostProcessingMillispreProcessingMilliscomputeMillisconfiguration
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" ], - "source": [ - "wcc_write_query = \"\"\"\n", - "\n", - "CALL gds.wcc.write('family', {writeProperty:'familyComponent'})\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(wcc_write_query)" + "text/plain": [ + " writeMillis nodePropertiesWritten componentCount \\\n", + "0 123 710 156 \n", + "\n", + " componentDistribution postProcessingMillis \\\n", + "0 {'p99': 139, 'min': 1, 'max': 321, 'mean': 4.5... 9 \n", + "\n", + " preProcessingMillis computeMillis \\\n", + "0 0 4 \n", + "\n", + " configuration \n", + "0 {'jobId': '6c933782-d1a8-4f5e-9748-fad56cebab3... " ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wcc_write_query = \"\"\"\n", + "\n", + "CALL gds.wcc.write('family', {writeProperty:'familyComponent'})\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(wcc_write_query)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "x1EWzmFORYVy", + "outputId": "5d433f56-7847-4b9e-e645-2d05b974c2fb" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "# Also need to mutate in order to be able to use subgraph later on\n", - "\n", - "wcc_mutate_query = \"\"\"\n", - "\n", - "CALL gds.wcc.mutate('family', {mutateProperty:'familyComponent'})\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(wcc_mutate_query)" + "data": { + "text/html": [ + "
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mutateMillisnodePropertiesWrittencomponentCountcomponentDistributionpostProcessingMillispreProcessingMilliscomputeMillisconfiguration
00710156{'p99': 139, 'min': 1, 'max': 321, 'mean': 4.5...505{'jobId': '7273e197-dde4-4aed-bf2f-7aebb547cca...
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" ], - "metadata": { - "id": "x1EWzmFORYVy", - "outputId": "5d433f56-7847-4b9e-e645-2d05b974c2fb", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "execution_count": 52, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " mutateMillis nodePropertiesWritten componentCount \\\n", - "0 0 699 147 \n", - "\n", - " componentDistribution postProcessingMillis \\\n", - "0 {'p99': 139, 'min': 1, 'max': 324, 'mean': 4.7... 4 \n", - "\n", - " preProcessingMillis computeMillis \\\n", - "0 0 16 \n", - "\n", - " configuration \n", - "0 {'seedProperty': None, 'consecutiveIds': False... " - ], - "text/html": [ - "\n", - "
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mutateMillisnodePropertiesWrittencomponentCountcomponentDistributionpostProcessingMillispreProcessingMilliscomputeMillisconfiguration
00699147{'p99': 139, 'min': 1, 'max': 324, 'mean': 4.7...4016{'seedProperty': None, 'consecutiveIds': False...
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 52 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EmTUYR0AHa4Y" - }, - "source": [ - "We will start by looking at the top five largest family components. The first thing we are interested in is which races are present in the family trees. We'll also add some random members in the results to get a better feeling of the data." + "text/plain": [ + " mutateMillis nodePropertiesWritten componentCount \\\n", + "0 0 710 156 \n", + "\n", + " componentDistribution postProcessingMillis \\\n", + "0 {'p99': 139, 'min': 1, 'max': 321, 'mean': 4.5... 5 \n", + "\n", + " preProcessingMillis computeMillis \\\n", + "0 0 5 \n", + "\n", + " configuration \n", + "0 {'jobId': '7273e197-dde4-4aed-bf2f-7aebb547cca... " ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Also need to mutate in order to be able to use subgraph later on\n", + "\n", + "wcc_mutate_query = \"\"\"\n", + "\n", + "CALL gds.wcc.mutate('family', {mutateProperty:'familyComponent'})\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(wcc_mutate_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EmTUYR0AHa4Y" + }, + "source": [ + "We will start by looking at the top five largest family components. The first thing we are interested in is which races are present in the family trees. We'll also add some random members in the results to get a better feeling of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 }, + "id": "awKFPFy7Ha4Y", + "outputId": "df34a048-a6ac-4bfa-f059-ea2d030feb14" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "id": "awKFPFy7Ha4Y", - "outputId": "df34a048-a6ac-4bfa-f059-ea2d030feb14", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " familyComponent size random_members \\\n", - "0 115 324 [Galadriel, Fingolfin, Amras] \n", - "1 0 139 [Frodo Baggins, Bilbo Baggins, Samwise Gamgee] \n", - "2 198 29 [Thorin II, Gimli, Balin] \n", - "3 273 21 [Túrin I, Dior of Gondor, Hador of Gondor] \n", - "4 99 6 [Aulë, Oromë, Tulkas] \n", - "\n", - " family_race \n", - "0 [Middle-earth elf, Maiar, men in Tolkien's leg... \n", - "1 [Hobbit] \n", - "2 [dwarves in Tolkien's legendarium] \n", - "3 [men in Tolkien's legendarium] \n", - "4 [valar] " - ], - "text/html": [ - "\n", - "
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familyComponentsizerandom_membersfamily_race
0115324[Galadriel, Fingolfin, Amras][Middle-earth elf, Maiar, men in Tolkien's leg...
10139[Frodo Baggins, Bilbo Baggins, Samwise Gamgee][Hobbit]
219829[Thorin II, Gimli, Balin][dwarves in Tolkien's legendarium]
327321[Túrin I, Dior of Gondor, Hador of Gondor][men in Tolkien's legendarium]
4996[Aulë, Oromë, Tulkas][valar]
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familyComponentsizerandom_membersfamily_race
00321[Galadriel, Fingolfin, Amras][Middle-earth elf, Maiar, Middle-earth man, ha...
18139[Frodo Baggins, Bilbo Baggins, Samwise Gamgee][Hobbit]
225929[Thorin II, Gimli, Balin][Middle-earth dwarf]
337821[Cirion, Eradan, Belegorn][Middle-earth man]
41576[Aulë, Oromë, Tulkas][Valar]
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" ], - "source": [ - "top5_families_query = \"\"\"\n", - "\n", - "MATCH (c:Character) \n", - "OPTIONAL MATCH (c)-[:BELONG_TO]->(race) \n", - "WITH c.familyComponent as familyComponent, \n", - " count(*) as size, \n", - " collect(c.name) as members, \n", - " collect(distinct race.race) as family_race \n", - "ORDER BY size DESC LIMIT 5 \n", - "RETURN familyComponent, \n", - " size, \n", - " members[..3] as random_members, \n", - " family_race\n", - "\"\"\"\n", - "\n", - "run_query(top5_families_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "68C5-8MQHa4Y" - }, - "source": [ - "As mentioned, the largest family has 328 members of various races ranging from elves to humans and even Maiar. It appears that elven and human lifes are quite intertwined in the Middle-earth. Also their legs. There is a reason why the half-elven race even exists. Other races like hobbits and dwarves stick more to their own kind.\n", - "\n", - "Let's examine the interracial marriages in the largest community." + "text/plain": [ + " familyComponent size random_members \\\n", + "0 0 321 [Galadriel, Fingolfin, Amras] \n", + "1 8 139 [Frodo Baggins, Bilbo Baggins, Samwise Gamgee] \n", + "2 259 29 [Thorin II, Gimli, Balin] \n", + "3 378 21 [Cirion, Eradan, Belegorn] \n", + "4 157 6 [Aulë, Oromë, Tulkas] \n", + "\n", + " family_race \n", + "0 [Middle-earth elf, Maiar, Middle-earth man, ha... \n", + "1 [Hobbit] \n", + "2 [Middle-earth dwarf] \n", + "3 [Middle-earth man] \n", + "4 [Valar] " ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top5_families_query = \"\"\"\n", + "\n", + "MATCH (c:Character) \n", + "OPTIONAL MATCH (c)-[:BELONG_TO]->(race) \n", + "WITH c.familyComponent as familyComponent, \n", + " count(*) as size, \n", + " collect(c.name) as members, \n", + " collect(distinct race.race) as family_race \n", + "ORDER BY size DESC LIMIT 5 \n", + "RETURN familyComponent, \n", + " size, \n", + " members[..3] as random_members, \n", + " family_race\n", + "\"\"\"\n", + "\n", + "run_query(top5_families_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "68C5-8MQHa4Y" + }, + "source": [ + "As mentioned, the largest family has 328 members of various races ranging from elves to humans and even Maiar. It appears that elven and human lifes are quite intertwined in the Middle-earth. Also their legs. There is a reason why the half-elven race even exists. Other races like hobbits and dwarves stick more to their own kind.\n", + "\n", + "Let's examine the interracial marriages in the largest community." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 }, + "id": "sCbdf4g0Ha4Y", + "outputId": "f7ea21ae-757d-41ac-c951-516678b7504f" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "id": "sCbdf4g0Ha4Y", - "outputId": "f7ea21ae-757d-41ac-c951-516678b7504f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 238 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " spouse_1 race_1 spouse_2 \\\n", - "0 Beren Erchamion men in Tolkien's legendarium Lúthien \n", - "1 Melian Maiar Thingol \n", - "2 Elrond half-elven Celebrían \n", - "3 Tuor men in Tolkien's legendarium Idril \n", - "4 Dior Eluchíl half-elven Nimloth \n", - "5 Arwen half-elven Aragorn \n", - "\n", - " race_2 \n", - "0 Middle-earth elf \n", - "1 Middle-earth elf \n", - "2 Middle-earth elf \n", - "3 Middle-earth elf \n", - "4 Middle-earth elf \n", - "5 men in Tolkien's legendarium " - ], - "text/html": [ - "\n", - "
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" ], - "source": [ - "ir_query = \"\"\"\n", - "\n", - "MATCH (c:Character) \n", - "WHERE c.familyComponent = 115 // fix the family component \n", - "MATCH p=(race)<-[:BELONG_TO]-(c)-[:SPOUSE]-(other)-[:BELONG_TO]->(other_race) \n", - "WHERE race <> other_race AND id(c) > id(other) \n", - "RETURN c.name as spouse_1, \n", - " race.race as race_1, \n", - " other.name as spouse_2, \n", - " other_race.race as race_2\n", - "\"\"\"\n", - "\n", - "run_query(ir_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ax5hKMTLHa4Z" - }, - "source": [ - "First of all, I didn't know that Elrond was a half-elf. It seems like the human and elven \"alliance\" is as old as time itself. I was mainly expecting to see Arwen and Aragorn as I remember that from the movies. It would be interesting to learn how far back do half-elves go. Let's look who are the half-elves with the most descendants." + "text/plain": [ + " spouse_1 race_1 spouse_2 race_2\n", + "0 Melian Maiar Thingol Middle-earth elf\n", + "1 Dior Eluchíl half-elven Nimloth Middle-earth elf\n", + "2 Beren Middle-earth man Lúthien Middle-earth elf\n", + "3 Elrond half-elven Celebrían Middle-earth elf\n", + "4 Tuor Middle-earth man Idril Middle-earth elf\n", + "5 Arwen half-elven Aragorn Middle-earth man" ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ir_query = \"\"\"\n", + "\n", + "MATCH (c:Character) \n", + "WHERE c.familyComponent = 0 // fix the family component \n", + "MATCH p=(race)<-[:BELONG_TO]-(c)-[:SPOUSE]-(other)-[:BELONG_TO]->(other_race) \n", + "WHERE race <> other_race AND id(c) > id(other) \n", + "RETURN c.name as spouse_1, \n", + " race.race as race_1, \n", + " other.name as spouse_2, \n", + " other_race.race as race_2\n", + "\"\"\"\n", + "\n", + "run_query(ir_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ax5hKMTLHa4Z" + }, + "source": [ + "First of all, I didn't know that Elrond was a half-elf. It seems like the human and elven \"alliance\" is as old as time itself. I was mainly expecting to see Arwen and Aragorn as I remember that from the movies. It would be interesting to learn how far back do half-elves go. Let's look who are the half-elves with the most descendants." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 }, + "id": "bXhIScloHa4Z", + "outputId": "eddf3509-03a5-4907-b151-721e0040a67d" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "id": "bXhIScloHa4Z", - "outputId": "eddf3509-03a5-4907-b151-721e0040a67d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " character descendants\n", - "0 Dior Eluchíl 11\n", - "1 Elwing 10\n", - "2 Eärendil 10\n", - "3 Elros 9\n", - "4 Elrond 2" - ], - "text/html": [ - "\n", - "
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characterdescendants
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" ], - "source": [ - "oldest_halfelf_query = \"\"\"\n", - "\n", - "MATCH (c:Character)\n", - "WHERE (c)-[:BELONG_TO]->(:Race{race:'half-elven'})\n", - "MATCH p=(c)<-[:HAS_FATHER|HAS_MOTHER*..20]-(end)\n", - "WHERE NOT (end)<-[:HAS_FATHER|:HAS_MOTHER]-()\n", - "WITH c, max(length(p)) as descendants\n", - "ORDER BY descendants DESC\n", - "LIMIT 5\n", - "RETURN c.name as character,\n", - " descendants\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(oldest_halfelf_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wOIj6SAGHa4Z" - }, - "source": [ - "It seems like Dior Eluchíl is the oldest recorded half-elf. I inspected results on LOTR fandom site, and it seems we are correct. Dior Eluchil was born in the First Age in the year 470. There are a couple of other half-elves who were born within 50 years of Dior.\n", - "\n", - "### Betweenness centrality\n", - "\n", - "We will also take a look at the betweenness centrality algorithm. It is used to find bridge nodes between different communities. If we take a look at the following visualization, we can observe that Captain America has the highest betweenness centrality score. That is because he is the main bridge in the network and connects the left-hand side of the network to the right-hand side. The second bridge in the network is the Beast. We can easily see that all the information exchanged between the main and right-hand side of the network has to go through him to reach the right-hand side.\n", - "\n", - "We will look for the bridge characters in the largest family network. My guess would be that spouses in an interracial marriage will come out on top. This is because all the communication between the races flows through them. We've seen that there are only six interracial marriages, so probably some of them will come out on top." + "text/plain": [ + " character descendants\n", + "0 Dior Eluchíl 11\n", + "1 Eärendil 10\n", + "2 Elwing 10\n", + "3 Elros 9\n", + "4 Elrond 2" ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oldest_halfelf_query = \"\"\"\n", + "\n", + "MATCH (c:Character)\n", + "WHERE exists{ (c)-[:BELONG_TO]->(:Race{race:'half-elven'}) }\n", + "MATCH p=(c)<-[:HAS_FATHER|HAS_MOTHER*..20]-(end)\n", + "WHERE NOT (end)<-[:HAS_FATHER|:HAS_MOTHER]-()\n", + "WITH c, max(length(p)) as descendants\n", + "ORDER BY descendants DESC\n", + "LIMIT 5\n", + "RETURN c.name as character,\n", + " descendants\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(oldest_halfelf_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wOIj6SAGHa4Z" + }, + "source": [ + "It seems like Dior Eluchíl is the oldest recorded half-elf. I inspected results on LOTR fandom site, and it seems we are correct. Dior Eluchil was born in the First Age in the year 470. There are a couple of other half-elves who were born within 50 years of Dior.\n", + "\n", + "### Betweenness centrality\n", + "\n", + "We will also take a look at the betweenness centrality algorithm. It is used to find bridge nodes between different communities. If we take a look at the following visualization, we can observe that Captain America has the highest betweenness centrality score. That is because he is the main bridge in the network and connects the left-hand side of the network to the right-hand side. The second bridge in the network is the Beast. We can easily see that all the information exchanged between the main and right-hand side of the network has to go through him to reach the right-hand side.\n", + "\n", + "We will look for the bridge characters in the largest family network. My guess would be that spouses in an interracial marriage will come out on top. This is because all the communication between the races flows through them. We've seen that there are only six interracial marriages, so probably some of them will come out on top." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "VOKMREFFRFMg", + "outputId": "e5381a6d-ad4a-4b30-c1bb-2139eb4e1c48" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "create_largest_wcc_query = \"\"\"\n", - "CALL gds.graph.project.cypher('largest-wcc', \n", - " 'MATCH (n:Character) WHERE n.familyComponent = 115 \n", - " RETURN id(n) as id',\n", - " 'MATCH (s:Character)-[:HAS_FATHER|HAS_MOTHER|SPOUSE|SIBLING]-(t:Character) \n", - " RETURN id(s) as source, id(t) as target',\n", - " {validateRelationships: false})\n", - "\"\"\"\n", - "\n", - "run_query(create_largest_wcc_query)" + "data": { + "text/html": [ + "
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nodeQueryrelationshipQuerygraphNamenodeCountrelationshipCountprojectMillis
0MATCH (n:Character) WHERE n.familyComponent = ...MATCH (s:Character)-[:HAS_FATHER|HAS_MOTHER|SP...largest-wcc321110834
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" ], - "metadata": { - "id": "VOKMREFFRFMg", - "outputId": "e5381a6d-ad4a-4b30-c1bb-2139eb4e1c48", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "execution_count": 58, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeQuery \\\n", - "0 MATCH (n:Character) WHERE n.familyComponent = ... \n", - "\n", - " relationshipQuery graphName nodeCount \\\n", - "0 MATCH (s:Character)-[:HAS_FATHER|HAS_MOTHER|SP... largest-wcc 324 \n", - "\n", - " relationshipCount projectMillis \n", - "0 1114 93 " - ], - "text/html": [ - "\n", - "
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nodeQueryrelationshipQuerygraphNamenodeCountrelationshipCountprojectMillis
0MATCH (n:Character) WHERE n.familyComponent = ...MATCH (s:Character)-[:HAS_FATHER|HAS_MOTHER|SP...largest-wcc324111493
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" ], - "source": [ - "betwenness_centrality_query = \"\"\"\n", - "\n", - "CALL gds.betweenness.stream('largest-wcc')\n", - "YIELD nodeId, score\n", - "RETURN gds.util.asNode(nodeId).name as character,\n", - " score\n", - "ORDER BY score DESC \n", - "LIMIT 10\n", - "\n", - "\"\"\"\n", - "\n", - "run_query(betwenness_centrality_query)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DbZGAkRUHa4a" - }, - "source": [ - "Interesting to see that Arwen and Aragorn come out on top. Not exactly sure why, but I keep on thinking that they are the modern Romeo and Juliet that have formed an alliance between men and half-elves with their marriage. I have no idea how the JRR Tolkien system for generating names worked, but it seems a bit biased towards names starting with an A." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "zHt9z2yyHa4a" - }, - "outputs": [], - "source": [ - "" + "text/plain": [ + " character score\n", + "0 Arwen 42750.000000\n", + "1 Aragorn 42222.000000\n", + "2 Arathorn II 40832.000000\n", + "3 Arador 40542.000000\n", + "4 Argonui 40248.000000\n", + "5 Arathorn I 39950.000000\n", + "6 Arassuil 39648.000000\n", + "7 Elrond 39371.107143\n", + "8 Arahad II 39342.000000\n", + "9 Aravorn 39032.000000" ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "scispacy", - "language": "python", - "name": "scispacy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - }, - "colab": { - "name": "Part1 Importing Wikidata into Neo4j and analyzing family trees.ipynb", - "provenance": [], - "include_colab_link": true - } + ], + "source": [ + "betwenness_centrality_query = \"\"\"\n", + "\n", + "CALL gds.betweenness.stream('largest-wcc')\n", + "YIELD nodeId, score\n", + "RETURN gds.util.asNode(nodeId).name as character,\n", + " score\n", + "ORDER BY score DESC \n", + "LIMIT 10\n", + "\n", + "\"\"\"\n", + "\n", + "run_query(betwenness_centrality_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DbZGAkRUHa4a" + }, + "source": [ + "Interesting to see that Arwen and Aragorn come out on top. Not exactly sure why, but I keep on thinking that they are the modern Romeo and Juliet that have formed an alliance between men and half-elves with their marriage. I have no idea how the JRR Tolkien system for generating names worked, but it seems a bit biased towards names starting with an A." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zHt9z2yyHa4a" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "include_colab_link": true, + "name": "Part1 Importing Wikidata into Neo4j and analyzing family trees.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/debug/node2vec.ipynb b/debug/node2vec.ipynb deleted file mode 100644 index de6f768..0000000 --- a/debug/node2vec.ipynb +++ /dev/null @@ -1,3714 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "source": [ - "!pip install neo4j node2vec networkx" - ], - "metadata": { - "id": "zyRBeIgMB5nI", - "outputId": "a7faee33-b8c6-4667-e18f-7a875a6d86e8", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "id": "zyRBeIgMB5nI", - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Requirement already satisfied: neo4j in /usr/local/lib/python3.7/dist-packages (4.4.5)\n", - "Requirement already satisfied: node2vec in /usr/local/lib/python3.7/dist-packages (0.4.3)\n", - "Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (2.6.3)\n", - "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j) (2022.1)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from node2vec) (1.21.6)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from node2vec) (4.64.0)\n", - "Requirement already satisfied: gensim in /usr/local/lib/python3.7/dist-packages (from node2vec) (3.6.0)\n", - "Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.7/dist-packages (from node2vec) (1.1.0)\n", - "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim->node2vec) (5.2.1)\n", - "Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from gensim->node2vec) (1.15.0)\n", - "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.7/dist-packages (from gensim->node2vec) (1.7.3)\n" - ] - } - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "799c8425", - "metadata": { - "id": "799c8425" - }, - "outputs": [], - "source": [ - "from neo4j import GraphDatabase\n", - "\n", - "url = 'bolt://44.193.1.247:7687'\n", - "username = 'neo4j'\n", - "password = 'fund-circulation-morale'\n", - "\n", - "# Connect to Neo4j\n", - "driver = GraphDatabase.driver(url, auth=(username, password))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e09e693c", - "metadata": { - "id": "e09e693c" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "def run_query(query):\n", - " with driver.session() as session:\n", - " result = session.run(query)\n", - " return pd.DataFrame([r.values() for r in result], columns=result.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "69c12633", - "metadata": { - "id": "69c12633", - "outputId": "e69f419e-dbda-417b-8b7c-e7cd28050dac", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " gds.version()\n", - "0 2.0.1" - ], - "text/html": [ - "\n", - "
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gds.version()
02.0.1
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nodeProjectionrelationshipProjectiongraphNamenodeCountrelationshipCountprojectMillis
0{'Stream': {'label': 'Stream', 'properties': {}}}{'SHARED_AUDIENCE': {'orientation': 'UNDIRECTE...twitch3721262854679
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 11 - } - ], - "source": [ - "run_query(\"\"\"\n", - "CALL gds.graph.project(\"twitch\", \"Stream\", \n", - " {SHARED_AUDIENCE: {orientation: \"UNDIRECTED\", properties:[\"weight\"]}})\n", - "\"\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3b0c05dc", - "metadata": { - "id": "3b0c05dc", - "outputId": "cc1e43a9-8169-4ee1-d5cf-9cf817909601", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "embedding dimension 8 has {'precision': 0.9018994255561046, 'recall': 0.9006711409395973, 'f1-score': 0.9005426180923415, 'support': 745} weighted avg\n", - "embedding dimension 16 has {'precision': 0.8888086789098827, 'recall': 0.8859060402684564, 'f1-score': 0.8853744481384653, 'support': 745} weighted avg\n", - "embedding dimension 32 has {'precision': 0.856411034595741, 'recall': 0.8469798657718121, 'f1-score': 0.845814552890966, 'support': 745} weighted avg\n", - "embedding dimension 64 has {'precision': 0.8281362773812437, 'recall': 0.7932885906040269, 'f1-score': 0.7852580524128767, 'support': 745} weighted avg\n", - "embedding dimension 128 has {'precision': 0.7924177877059633, 'recall': 0.7100671140939597, 'f1-score': 0.684787735858872, 'support': 745} weighted avg\n", - "embedding dimension 256 has {'precision': 0.7442489869651336, 'recall': 0.6067114093959731, 'f1-score': 0.529712561742786, 'support': 745} weighted avg\n" - ] - } - ], - "source": [ - "from sklearn.metrics import classification_report\n", - "\n", - "for embeddingDimension in [8,16,32,64,128,256]:\n", - " data = run_query(f\"\"\"\n", - " CALL gds.beta.node2vec.stream('twitch', \n", - " {{embeddingDimension:{embeddingDimension}, relationshipWeightProperty:'weight',\n", - " inOutFactor:2, returnFactor:1}})\n", - " YIELD nodeId, embedding\n", - " WITH gds.util.asNode(nodeId) AS node, embedding\n", - " RETURN node.id AS streamId, node.language AS language, embedding\n", - " \"\"\")\n", - " data['output'] = pd.factorize(data['language'])[0]\n", - " X = data['embedding'].to_list()\n", - " y = data['output'].to_list()\n", - "\n", - " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0)\n", - "\n", - " rfc = RandomForestClassifier()\n", - " rfc.fit(X_train, y_train)\n", - "\n", - " y_pred = rfc.predict(X_test)\n", - " r = classification_report(y_test,y_pred, output_dict=True)['weighted avg']\n", - " print(f\"embedding dimension {embeddingDimension} has {r} weighted avg\")" - ] - }, - { - "cell_type": "code", - "source": [ - "run_query(\"\"\"\n", - "CALL gds.graph.drop('twitch')\n", - "\"\"\")" - ], - "metadata": { - "id": "Tee7V3iIJYBK", - "outputId": "c13402d2-b2e9-4480-f91b-842f695803a0", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 142 - } - }, - "id": "Tee7V3iIJYBK", - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " graphName database memoryUsage sizeInBytes nodeCount relationshipCount \\\n", - "0 twitch neo4j -1 3721 262854 \n", - "\n", - " configuration density \\\n", - "0 {'relationshipProjection': {'SHARED_AUDIENCE':... 0.018989 \n", - "\n", - " creationTime modificationTime \\\n", - "0 2022-08-08T16:25:31.018810000+00:00 2022-08-08T16:25:31.695509000+00:00 \n", - "\n", - " schema \n", - "0 {'relationships': {'SHARED_AUDIENCE': {'weight... " - ], - "text/html": [ - "\n", - "
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graphNamedatabasememoryUsagesizeInBytesnodeCountrelationshipCountconfigurationdensitycreationTimemodificationTimeschema
0twitchneo4j-13721262854{'relationshipProjection': {'SHARED_AUDIENCE':...0.0189892022-08-08T16:25:31.018810000+00:002022-08-08T16:25:31.695509000+00:00{'relationships': {'SHARED_AUDIENCE': {'weight...
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 13 - } - ] - }, - { - "cell_type": "code", - "source": [ - "from node2vec import Node2Vec\n", - "import networkx as nx" - ], - "metadata": { - "id": "wbTd9nR2EMD1" - }, - "id": "wbTd9nR2EMD1", - "execution_count": 14, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# networx Graph" - ], - "metadata": { - "id": "TaOUWr_NHuq7" - }, - "id": "TaOUWr_NHuq7", - "execution_count": 15, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Construct a networkX graph\n", - "edge_list = run_query(\"\"\"\n", - "MATCH (s:Stream)-[r:SHARED_AUDIENCE]->(t:Stream)\n", - "WITH toString(s.id) + \" \" + toString(t.id) + \" {'weight':\" + toString(r.weight) + \"}\" as edge\n", - "WITH collect(edge) as result\n", - "RETURN result\n", - "\"\"\")\n", - "\n", - "edge_list = edge_list['result'].to_list()[0]\n", - "# Undirected graph as well\n", - "G = nx.parse_edgelist(edge_list, create_using=nx.Graph(), nodetype=int)" - ], - "metadata": { - "id": "Ns3aWDwSIIJC" - }, - "id": "Ns3aWDwSIIJC", - "execution_count": 16, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "G.number_of_nodes()" - ], - "metadata": { - "id": "a0IBZGBUIS4V", - "outputId": "a4ebf627-2816-480f-a81a-90c971a52ef5", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "id": "a0IBZGBUIS4V", - "execution_count": 17, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "3721" - ] - }, - "metadata": {}, - "execution_count": 17 - } - ] - }, - { - "cell_type": "code", - "source": [ - "labels = run_query(\"\"\"\n", - "MATCH (s:Stream)\n", - "RETURN s.id AS id, s.language AS language\n", - "\"\"\")" - ], - "metadata": { - "id": "cb5xotXzPblA" - }, - "id": "cb5xotXzPblA", - "execution_count": 18, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "for embeddingDimension in [8,16,32,64,128,256]:\n", - " node2vec = Node2Vec(G, dimensions=embeddingDimension, walk_length=80, num_walks=10, workers=4, p=2, q= 1, weight_key= 'weight', seed=1)\n", - " model = node2vec.fit(window=10, min_count=1, batch_words=1000, sg=1, negative=5, ns_exponent=0.75, alpha=0.01, min_alpha=0.0001) # Any keywords acceptable by gensim.Word2Vec can be passed, `dimensions` and `workers` are automatically passed (from the Node2Vec constructor)\n", - " d = []\n", - " for i in model.wv.vocab:\n", - " d.append({'id': i, 'embedding': list(model.wv[i])})\n", - " df = pd.DataFrame.from_dict(d).merge(labels, on='id')\n", - " df['output'] = pd.factorize(df['language'])[0]\n", - " X = df['embedding'].to_list()\n", - " y = df['output'].to_list()\n", - "\n", - " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0)\n", - "\n", - " rfc = RandomForestClassifier()\n", - " rfc.fit(X_train, y_train)\n", - "\n", - " y_pred = rfc.predict(X_test)\n", - " r = classification_report(y_test,y_pred, output_dict=True)['weighted avg']\n", - " print(f\"embedding dimension {embeddingDimension} has {r} weighted avg\")" - ], - "metadata": { - "id": "93A-B844PEXt", - "outputId": "a368fd33-a4bc-4bc2-fda7-056c9d8307e4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 313, - "referenced_widgets": [ - "7d48ec744e224173870600fc760f2998", - "1f36d43087694874af3fb7838ab75718", - "fa831425ec364fecb40967b7a7026144", - "713ec0557c9d469ab88d3d79d41c72c2", - "62f7eb2507064130851367060903c095", - "9d77f98d702c46c49dca0fd0adb04833", - "86f0a9f3e153460aba378992bca35461", - "6b9967aa7c5c405ba886b2a051e7c827", - "77e4fc97d5fa4bc693b5cf664f8ddb18", - "e7b50c5b37844b8ea6bb8ed0688796f9", - "ad424d63e66f4bf594faf1e3b9be6c48", - "5da7501823b14e579d8944d4e09b102f", - "c21ecdef751e48b2ac92c58333df5dda", - "263ee46a75aa4695b63a5153755c1594", - "c587a840144a4698bf66b50d81f1eead", - "6f492e7bafca4e459ed7b9978fa1a6d3", - "9cbc3e392bd442e2975bf672e2b142b1", - "e196bf399cef438dae1b6bb077f5c1cb", - "93765946bce14dc888aa7bf206bfa5f0", - "22240b45ad004bbe9e52c35f82040666", - "66b1a73744844a878bbc816d11336e19", - "a2b9675c524144caadbd81d5f39baa54", - "73b3b66988754dc18c22d6294e198609", - "08643dfc8bc54acd84d464d7487df50e", - "2af5e6c1efba4cc88139fc05ea0c0006", - "ad5e4261b3064e45b777e3a56d116e38", - "d8012cd7fbe24711b4ec44cbc416f8da", - "ac6ae0fd76e341f4b3523f3ef09473d9", - "02aca957089449df921dac6b1f5829de", - "47e5afbab7614af399a73c3c8c6adcef", - "5e1620568118434abef3c8716f71fd0c", - "325a8dbac4ad43d9817e75617504da99", - "dc839ef080e04c678bb99bf29672d6f4", - "b3161e41568947c7a2fdb3ce50078b23", - "36be16b5566e4a89b1cde78ba29c7e6f", - "620963486e7748afb292bb42d7d2ffa8", - "0816a64432e049da9acb986911594a53", - "c5876824b2d24d42bc7cce7e2cf35b27", - "792a1f635d88464a880cc1045f04b432", - "7ede0a7b4725455a80eedcff98918e6b", - "19d3560ce3da46f5a3c62f846e7c9aa1", - "320d9b85a4f14e5cad3550e59f7e9bb8", - "c8c33153b9e4489c92beb9603f10c555", - "642c00821930488d88d9a0d561ecb8c4", - "4116b586ad15456292b979cbe07fe7b1", - "c1bcb300bd6e4ba895165f64e419e80b", - "3fbaaa6279cd430aac86b5f400662c35", - "e0761bd0fdc94cea8c9cde8f6cd703b8", - "d3aa40f4a4474ab392421c9b366581dc", - "7e3bf1b662b349d2bc0536c9851d15ca", - "80307d5925344ca68ad75fe27c020594", - "84529df93bcb4afcbb3c3ee127224cee", - "00b7c3a83bd84f5e8eee99513a72d866", - "32039480493c4a1fbb6ddf8398857d37", - "b1ccf9a81e7340acbbf404df9d7dd85d", - "a4a30e68cd7746039010701fc162126f", - "fef877ff5b294d1982fdbc3860e5d164", - "3d8ae4ea77eb450983cb0d778acc9b68", - "6b9d461efb5a438e86cf6a1866d4a72b", - "aca99435049044a5a27e0099b4fb559c", - "6487a8dbe76b433b9e438ffec5569d43", - "1d0e2799125142e4851447250e6ea20b", - "a49d2f591e3a438f86748726ef428358", - "01608bf7baea4a6e96e1e2b06958d644", - "6defc8202f744529bb55aa742ca4d3cd", - "c61852112cbc4b1db95c15da7f5f1570" - ] - } - }, - "id": "93A-B844PEXt", - "execution_count": 19, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Computing transition probabilities: 0%| | 0/3721 [00:00()) AS numberOfDevices,\n", - " size((u)-[:HAS_CC]->()) AS numberOfCCs,\n", - " size((u)-[:HAS_IP]->()) AS numberOfIps,\n", + " count{ (u)-[:USED]->() } AS numberOfDevices,\n", + " count{ (u)-[:HAS_CC]->() } AS numberOfCCs,\n", + " count{ (u)-[:HAS_IP]->() } AS numberOfIps,\n", " coalesce(totalOutgoingAmount, 0) AS totalOutgoingAmount, \n", " coalesce(avgOutgoingAmount, 0) AS avgOutgoingAmount,\n", " coalesce(maxOutgoingAmount, 0) AS maxOutgoingAmount,\n", @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "3ca0b417", "metadata": {}, "outputs": [ @@ -314,7 +314,7 @@ "4 200.00 2 " ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -333,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "66632cba", "metadata": {}, "outputs": [ @@ -383,7 +383,7 @@ "1 1 211" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -402,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "74048cdd", "metadata": {}, "outputs": [ @@ -608,7 +608,7 @@ "max 6750.000000 564.000000 " ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -629,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "cb367423", "metadata": {}, "outputs": [ @@ -639,7 +639,7 @@ "" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -681,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "d3abee73", "metadata": {}, "outputs": [], @@ -698,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "2b049b2f", "metadata": {}, "outputs": [], @@ -746,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "19e86468", "metadata": {}, "outputs": [ @@ -824,10 +824,25 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "dc970138", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ef751e6905144d54a17f4adebc534a68", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading: 0%| | 0/100 [00:00\n", " \n", " 0\n", - " 0\n", + " 765184\n", " 0\n", " \n", " \n", " 1\n", - " 1\n", + " 765185\n", " 1\n", " \n", " \n", " 2\n", - " 2\n", + " 765186\n", " 2\n", " \n", " \n", " 3\n", - " 3\n", + " 765187\n", " 3\n", " \n", " \n", " 4\n", - " 4\n", + " 765188\n", " 4\n", " \n", " \n", @@ -953,14 +968,14 @@ ], "text/plain": [ " nodeId componentId\n", - "0 0 0\n", - "1 1 1\n", - "2 2 2\n", - "3 3 3\n", - "4 4 4" + "0 765184 0\n", + "1 765185 1\n", + "2 765186 2\n", + "3 765187 3\n", + "4 765188 4" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -979,7 +994,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "e97c1869", "metadata": {}, "outputs": [], @@ -1000,7 +1015,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "340f4bf1", "metadata": {}, "outputs": [], @@ -1025,7 +1040,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "9983c200", "metadata": {}, "outputs": [], @@ -1051,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "9d41c3aa", "metadata": {}, "outputs": [], @@ -1073,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "3a86c2e3", "metadata": {}, "outputs": [], @@ -1096,7 +1111,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "0b914683", "metadata": {}, "outputs": [ @@ -1167,28 +1182,29 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "ccd028fd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "graphName fraud\n", - "database neo4j\n", - "memoryUsage \n", - "sizeInBytes -1\n", - "nodeCount 152550\n", - "relationshipCount 171201\n", - "configuration {'relationshipProjection': {'HAS_CC': {'orient...\n", - "density 0.000007\n", - "creationTime 2022-06-29T11:32:01.574618000+02:00\n", - "modificationTime 2022-06-29T11:32:01.917006000+02:00\n", - "schema {'graphProperties': {}, 'relationships': {'HAS...\n", + "graphName fraud\n", + "database neo4j\n", + "memoryUsage \n", + "sizeInBytes -1\n", + "nodeCount 152550\n", + "relationshipCount 171201\n", + "configuration {'relationshipProjection': {'HAS_CC': {'orient...\n", + "density 0.000007\n", + "creationTime 2023-02-01T13:10:50.719251667+01:00\n", + "modificationTime 2023-02-01T13:10:51.453140320+01:00\n", + "schema {'graphProperties': {}, 'relationships': {'HAS...\n", + "schemaWithOrientation {'graphProperties': {}, 'relationships': {'HAS...\n", "Name: 0, dtype: object" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } diff --git a/gds_python/gds_python_intro.ipynb b/gds_python/gds_python_intro.ipynb index 1811a52..d7e9341 100644 --- a/gds_python/gds_python_intro.ipynb +++ b/gds_python/gds_python_intro.ipynb @@ -1,1707 +1,1307 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kHob0RF8YDrx" + }, + "source": [ + "# How to get started with the Neo4j Graph Data Science Python client\n", + "## Learn the basic syntax of the newly released Python client for Neo4j Graph Data Science library\n", + "\n", + "Data scientists like me love Python. It features a wide variety of machine learning and data science libraries that can help you get started on a data science project in minutes. It is not uncommon to use a variety of libraries in a data science workflow. With the release of version 2 of the Neo4j Graph Data Science (GDS) library, a supporting Python client has been introduced. The Python client for the GDS library is designed to help you seamlessly integrate the Neo4j Graph Data Science library into your data science workflow. Instead of having to write Cypher statements to execute graph algorithms, the Python client provides a simple surface that allows you to project and run graph algorithms using pure Python code.\n", + "\n", + "Since the Python client for GDS is relatively new, there are not many examples out there yet. Therefore, I've decided to write this blog post to help you get started with the GDS Python client syntax and show some common usage patterns through a simple network analysis.\n", + "\n", + "The Neo4j Graph Data Science Python client can be installed using the pip package installer." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { "colab": { - "name": "gds-python.ipynb", - "provenance": [], - "authorship_tag": "ABX9TyMLODtjsTX2gWhXe5ADDUdP", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "base_uri": "https://localhost:8080/" }, - "language_info": { - "name": "python" + "id": "Q1KlTgmR8PAL", + "outputId": "db6489bd-cf34-4f97-8b44-77f08b3e9b73" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: graphdatascience in /usr/local/lib/python3.7/dist-packages (1.0.0)\n", + "Requirement already satisfied: pandas<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (1.3.5)\n", + "Requirement already satisfied: neo4j<5.0,>=4.4.2 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (4.4.3)\n", + "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j<5.0,>=4.4.2->graphdatascience) (2022.1)\n", + "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (1.21.6)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas<2.0,>=1.0->graphdatascience) (1.15.0)\n" + ] } + ], + "source": [ + "!pip install graphdatascience" + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] + { + "cell_type": "markdown", + "metadata": { + "id": "tv2vtJOQYLQW" + }, + "source": [ + "An important thing to note is that the Python client is only guaranteed to work with GDS versions 2.0 and later. Therefore, if you have a previous version, I suggest you first upgrade the GDS library to the latest version.\n", + "# Neo4j environment setup\n", + "If you want to follow along with the code examples, you need to set up a Neo4j database. I suggest you use a [blank project on Neo4j Sandbox](https://sandbox.neo4j.com/?usecase=blank-sandbox) for this simple demonstration, but you can also download a [Neo4j Desktop application](https://neo4j.com/download/) and set up a local database.\n", + "\n", + "Neo4j Sandbox has the GDS library already installed. However, if you use Neo4j Desktop, you have to install the GDS library manually.\n", + "\n", + "# Setting up the GDS Python client connection\n", + "We start by defining the client connection to the Neo4j database. If you have seen any of my previous blog posts that use the official Neo4j Python driver, you can see that the syntax is almost identical." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "GKTUobMl8S31" + }, + "outputs": [], + "source": [ + "from graphdatascience import GraphDataScience\n", + "\n", + "host = \"bolt://44.193.28.203:7687\"\n", + "user = \"neo4j\"\n", + "password= \"combatants-coordinates-tugs\"\n", + "\n", + "gds = GraphDataScience(host, auth=(user, password))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FWOhP1TdYehq" + }, + "source": [ + "We have instantiated the connection to the Neo4j instance. If you are using Neo4j Enterprise, you might have multiple databases available in Neo4j. If we want to use any database other than the default one, we can select the required database using the set_database method." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "lk93jlvJ-u6L" + }, + "outputs": [], + "source": [ + "# Optionally set different database\n", + "#gds.set_database(\"databaseName\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rZbDBeZnYhEJ" + }, + "source": [ + "Lastly, we can verify that the connection is valid and the target Neo4j instance has the GDS library installed by using the `gds.version()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "-Ah7nqZD6Zcd", + "outputId": "42ae6efd-561d-45e5-d601-27d5e562c580" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "# How to get started with the Neo4j Graph Data Science Python client\n", - "## Learn the basic syntax of the newly released Python client for Neo4j Graph Data Science library\n", - "\n", - "Data scientists like me love Python. It features a wide variety of machine learning and data science libraries that can help you get started on a data science project in minutes. It is not uncommon to use a variety of libraries in a data science workflow. With the release of version 2 of the Neo4j Graph Data Science (GDS) library, a supporting Python client has been introduced. The Python client for the GDS library is designed to help you seamlessly integrate the Neo4j Graph Data Science library into your data science workflow. Instead of having to write Cypher statements to execute graph algorithms, the Python client provides a simple surface that allows you to project and run graph algorithms using pure Python code.\n", - "\n", - "Since the Python client for GDS is relatively new, there are not many examples out there yet. Therefore, I've decided to write this blog post to help you get started with the GDS Python client syntax and show some common usage patterns through a simple network analysis.\n", - "\n", - "The Neo4j Graph Data Science Python client can be installed using the pip package installer." - ], - "metadata": { - "id": "kHob0RF8YDrx" - } + "name": "stdout", + "output_type": "stream", + "text": [ + "2.3.0\n" + ] + } + ], + "source": [ + "# Check if connection is valid and the target database has GDS installed\n", + "print(gds.version())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ad85KnNeYlwa" + }, + "source": [ + "The version() method should return the version of the installed GDS library. If it returns anything else, make sure that you entered the correct credentials and the GDS library is installed.\n", + "# Executing Cypher statements\n", + "The Python client allows you to execute arbitrary Cypher statements using the `run_cypher` method. The method takes two parameters are input. The first and mandatory parameter is the Cypher query you want to execute. The second method parameter is optional and can be used to provide any query parameters.\n", + "\n", + "The `run_cypher` method can be used to import, transform, or fetch any data from the database. We will begin by populating the database with the [Harry Potter network](https://medium.com/neo4j/turn-a-harry-potter-book-into-a-knowledge-graph-ffc1c45afcc8) I created in one of my previous blog posts.\n", + "\n", + "The network contains characters in the first book, and their interactions, which are represented as relationships. The CSV with the relationship is available on my GitHub, so we can use the `LOAD CSV` clause to retrieve the data from GitHub and store it into Neo4j." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49 }, + "id": "GaXfepPu_YZk", + "outputId": "390e4c6d-d5b3-4191-e1ba-0437f8752363" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Q1KlTgmR8PAL", - "outputId": "db6489bd-cf34-4f97-8b44-77f08b3e9b73" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Requirement already satisfied: graphdatascience in /usr/local/lib/python3.7/dist-packages (1.0.0)\n", - "Requirement already satisfied: pandas<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (1.3.5)\n", - "Requirement already satisfied: neo4j<5.0,>=4.4.2 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (4.4.3)\n", - "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j<5.0,>=4.4.2->graphdatascience) (2022.1)\n", - "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (1.21.6)\n", - "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (2.8.2)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas<2.0,>=1.0->graphdatascience) (1.15.0)\n" - ] - } + "data": { + "text/html": [ + "
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" ], - "source": [ - "!pip install graphdatascience" + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = \"\"\"\n", + "LOAD CSV WITH HEADERS FROM $url AS row\n", + "MERGE (s:Character {name:row.source})\n", + "MERGE (t:Character {name:row.target})\n", + "MERGE (s)-[i:INTERACTS]->(t)\n", + "SET i.weight = toInteger(row.weight)\n", + "\"\"\"\n", + "params = {'url': 'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/HP/hp_1.csv'}\n", + "gds.run_cypher(query, params)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "egAEqSvfY6lz" + }, + "source": [ + "The import script uses the `run_cypher` method to execute the Cypher statement used to import the Harry Potter network. To demonstrate how Cypher parameters work with the `run_cypher` method, I've attached the URL of the file as a Cypher parameter. While the Cypher query is represented as a string, the Cypher parameters are defined as a dictionary.\n", + "If you have done any data analysis in Python, you have probably used the Pandas library in your workflow. Therefore, when fetching data from a database using the run_cyphermethod, the method conveniently returns a populated Pandas DataFrame. Having the data available as a Pandas DataFrame makes it much easier to integrate the data from Neo4j into your analytical workflow and use it in combination with other libraries.\n", + "\n", + "In this example, we will retrieve the degree (count of relationships) for each character in the network using the `run_cypher` method." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 }, + "id": "a50_Aq0GAiHR", + "outputId": "fb6dabb6-edda-4ae4-d924-65618ae9c5f3" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "An important thing to note is that the Python client is only guaranteed to work with GDS versions 2.0 and later. Therefore, if you have a previous version, I suggest you first upgrade the GDS library to the latest version.\n", - "# Neo4j environment setup\n", - "If you want to follow along with the code examples, you need to set up a Neo4j database. I suggest you use a [blank project on Neo4j Sandbox](https://sandbox.neo4j.com/?usecase=blank-sandbox) for this simple demonstration, but you can also download a [Neo4j Desktop application](https://neo4j.com/download/) and set up a local database.\n", - "\n", - "Neo4j Sandbox has the GDS library already installed. However, if you use Neo4j Desktop, you have to install the GDS library manually.\n", - "\n", - "# Setting up the GDS Python client connection\n", - "We start by defining the client connection to the Neo4j database. If you have seen any of my previous blog posts that use the official Neo4j Python driver, you can see that the syntax is almost identical." - ], - "metadata": { - "id": "tv2vtJOQYLQW" - } - }, - { - "cell_type": "code", - "source": [ - "from graphdatascience import GraphDataScience\n", - "\n", - "host = \"bolt://54.172.168.40:7687\"\n", - "user = \"neo4j\"\n", - "password= \"shares-masses-turnarounds\"\n", - "\n", - "gds = GraphDataScience(host, auth=(user, password))" - ], - "metadata": { - "id": "GKTUobMl8S31" - }, - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "We have instantiated the connection to the Neo4j instance. If you are using Neo4j Enterprise, you might have multiple databases available in Neo4j. If we want to use any database other than the default one, we can select the required database using the set_database method." - ], - "metadata": { - "id": "FWOhP1TdYehq" - } - }, - { - "cell_type": "code", - "source": [ - "# Optionally set different database\n", - "#gds.set_database(\"databaseName\")" - ], - "metadata": { - "id": "lk93jlvJ-u6L" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Lastly, we can verify that the connection is valid and the target Neo4j instance has the GDS library installed by using the `gds.version()` method." - ], - "metadata": { - "id": "rZbDBeZnYhEJ" - } - }, - { - "cell_type": "code", - "source": [ - "# Check if connection is valid and the target database has GDS installed\n", - "print(gds.version())" + "data": { + "text/html": [ + "
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" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-Ah7nqZD6Zcd", - "outputId": "42ae6efd-561d-45e5-d601-27d5e562c580" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "2.0.1\n" - ] - } + "text/plain": [ + " character degree\n", + "0 Petunia Dursley 8\n", + "1 Dudley Dursley 14\n", + "2 Lily J. Potter 5\n", + "3 James Potter I 5\n", + "4 Harry Potter 83" ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "degree_df = gds.run_cypher(\"\"\"\n", + "MATCH (c:Character)\n", + "RETURN c.name AS character,\n", + " count{ (c)--() } AS degree\n", + "\"\"\")\n", + "\n", + "degree_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mEsbwfP3ZGdj" + }, + "source": [ + "Since the data is available as a Pandas DataFrame, we can easily integrate it into our analytical workflow. For example, we can use the Seaborn library to visualize the node degree distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 530 }, + "id": "DhCt2ueTA4SA", + "outputId": "27ba275a-76c5-4a07-b798-46e0f0e261c5" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "The version() method should return the version of the installed GDS library. If it returns anything else, make sure that you entered the correct credentials and the GDS library is installed.\n", - "# Executing Cypher statements\n", - "The Python client allows you to execute arbitrary Cypher statements using the `run_cypher` method. The method takes two parameters are input. The first and mandatory parameter is the Cypher query you want to execute. The second method parameter is optional and can be used to provide any query parameters.\n", - "\n", - "The `run_cypher` method can be used to import, transform, or fetch any data from the database. We will begin by populating the database with the [Harry Potter network](https://medium.com/neo4j/turn-a-harry-potter-book-into-a-knowledge-graph-ffc1c45afcc8) I created in one of my previous blog posts.\n", - "\n", - "The network contains characters in the first book, and their interactions, which are represented as relationships. The CSV with the relationship is available on my GitHub, so we can use the `LOAD CSV` clause to retrieve the data from GitHub and store it into Neo4j." - ], - "metadata": { - "id": "Ad85KnNeYlwa" - } - }, - { - "cell_type": "code", - "source": [ - "query = \"\"\"\n", - "LOAD CSV WITH HEADERS FROM $url AS row\n", - "MERGE (s:Character {name:row.source})\n", - "MERGE (t:Character {name:row.target})\n", - "MERGE (s)-[i:INTERACTS]->(t)\n", - "SET i.weight = toInteger(row.weight)\n", - "\"\"\"\n", - "params = {'url': 'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/HP/hp_1.csv'}\n", - "gds.run_cypher(query, params)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49 - }, - "id": "GaXfepPu_YZk", - "outputId": "390e4c6d-d5b3-4191-e1ba-0437f8752363" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: []" - ], - "text/html": [ - "\n", - "
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\n", + "text/plain": [ + "
" ] - }, - { - "cell_type": "markdown", - "source": [ - "Since the data is available as a Pandas DataFrame, we can easily integrate it into our analytical workflow. For example, we can use the Seaborn library to visualize the node degree distribution." - ], - "metadata": { - "id": "mEsbwfP3ZGdj" - } - }, + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "sns.displot(data=degree_df, x=\"degree\", height=7, aspect=1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fkER78KrZOxT" + }, + "source": [ + "We can easily observe that most nodes have less than 15 relationships. However, there is one outlier in the dataset with 83 connections, and that is, of course, Harry Potter himself.\n", + "# Projected graph object\n", + "The central concept of the GDS Python client is to allow projecting and executing graph algorithms in Neo4j with pure Python code. Furthermore, the Python client is designed to mimic the GDS Cypher procedures so that we don't have to learn a new syntax to use the Python client.\n", + "\n", + "As you might know, before we can execute any graph algorithms, we first have to project an in-memory graph. For example, let's say we want to project a simple directed network of characters and their interactions with the Python client.\n", + "\n", + "![mapping.png](data:image/png;base64,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)\n", + "\n", + "If you are familiar with Cypher procedures of the Graph Data Science library, you will be able to pick up the Python client syntax easily. For the most part, we remove the CALLclause before the GDS procedures, and we get the Python client syntax to project graphs or execute algorithms.\n", + "\n", + "In our case, we want to project a network of characters where the interaction relationships are treated as undirected. Therefore, we must use the extended map syntax to define undirected relationships.\n", + "\n", + "![Copy of mapping_graph.drawio 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)\n", + "\n", + "When dealing with map objects, or dictionaries as they are called in Python, we have to add quotes around map keys. Otherwise, the keys would be treated as variables in Python, and you would get a NameError as the key variables are not defined. So, apart from adding quotes and removing the CALLclause, the syntax to project an in-memory graph is identical. \n", + "\n", + "When projecting a graph with the Python client, a client-side reference to the projected graph is returned. We call these references Graph objects. Along with the Graph object, the metadata from the procedure call is returned as Pandas Series.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "c78q70AkB1ZQ" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "sns.displot(data=degree_df, x=\"degree\", height=7, aspect=1.5)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 530 - }, - "id": "DhCt2ueTA4SA", - "outputId": "27ba275a-76c5-4a07-b798-46e0f0e261c5" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "36cbe152ce7d42dea0d178c4d05d76ad", + "version_major": 2, + "version_minor": 0 }, - "execution_count": 7, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 7 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": { - "needs_background": "light" - } - } + "text/plain": [ + "Loading: 0%| | 0/100 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nodeIdscore
001.851142
113.241780
220.375610
330.375610
4424.197442
\n", + "" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xll1y1m9DsH5", - "outputId": "1351780f-5673-4195-f10c-fd79a2b5338e" - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "hp-graph\n", - "2341 KiB\n", - "0.05782652043868395\n" - ] - } + "text/plain": [ + " nodeId score\n", + "0 0 1.851142\n", + "1 1 3.241780\n", + "2 2 0.375610\n", + "3 3 0.375610\n", + "4 4 24.197442" ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# PageRank stream\n", + "pagerank_df = gds.pageRank.stream(G, relationshipWeightProperty=\"weight\")\n", + "pagerank_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EuKnJ7Yzacb-" + }, + "source": [ + "The `stream` mode of any algorithm in the GDS library returns a stream of records. Python client then automatically converts the output into a Pandas DataFrame.\n", + "\n", + "If you have ever executed the `stream` mode of the graph algorithms in Neo4j GDS library, you might be aware that the result contains internal node ids as a reference to nodes instead of actual node objects. The `pagerank_df` DataFrame contains two columns:\n", + "* nodeId: Internal node ids used to reference nodes\n", + "* score: PageRank score\n", + "\n", + "We can retrieve the referenced node objects using the `nodeId` column without constructing a Cypher statement by using the `gds.util.asNodes()` method. The `gds.util.asNodes()` method takes a list of internal node ids as input and outputs a list of node objects." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "xTl4i5cPWbrh" + }, + "outputs": [], + "source": [ + "# If you need to fetch information about node objects based on their internal node ids, you can use gds.util.asNodes\n", + "pagerank_df['node_object'] = gds.util.asNodes(pagerank_df['nodeId'].to_list())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fa-iw0etaubY" + }, + "source": [ + "The `node_object` column now contains the referenced node objects. Node objects are defined in the underlying Neo4j Python driver. You can reference the [official documentation if you want to examine all the possible methods of the node object](https://neo4j.com/docs/api/python-driver/current/api.html#node).\n", + "\n", + "In this example, we will extract the `name` property from node objects and then visualize a bar chart of the top ten characters with the highest PageRank score." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 652 }, + "id": "6drUBiMBWqpJ", + "outputId": "9e3c906f-7d87-4404-d231-36f6fc0639cf" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "# Running graph algorithms\n", - "Now that we have the projected graph ready and available as the reference variable `G` , we can go ahead and execute a couple of graph algorithms using the Python client.\n", - "\n", - "We will begin by executing the weighted variant of the PageRank algorithm. The `stream` mode of the algorithm returns the result of the algorithm as a stream of records.\n", - "\n", - "![gds_pagerank.drawio 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- "\n", - "Similar to before, when we were projecting an in-memory graph, we need to remove the `CALL` clause in the Python client for all algorithm executions. We reference the projected graph by its name with the Cypher procedure statement. However, using the Python client, we pass the Graph object as the reference to the projected in-memory graph instead of its name. Lastly, any algorithm configuration parameters can be specified as keyword arguments in the Python client.\n", - "We can use the following Python script to execute the `stream` mode of the weighted PageRank algorithm." - ], - "metadata": { - "id": "so0n--2RaJzn" - } - }, - { - "cell_type": "code", - "source": [ - "# PageRank stream\n", - "pagerank_df = gds.pageRank.stream(G, relationshipWeightProperty=\"weight\")\n", - "pagerank_df.head()" - ], - "metadata": { - "id": "a0pLRI1XV5gi", - "outputId": "942c4d98-a44a-4875-f488-04877744d15a", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - } - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeId score\n", - "0 0 1.851142\n", - "1 1 3.241780\n", - "2 2 0.375610\n", - "3 3 0.375610\n", - "4 4 24.197442" - ], - "text/html": [ - "\n", - "
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 11 - } + "data": { + "text/plain": [ + "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n", + " [Text(0, 0, 'Harry Potter'),\n", + " Text(1, 0, 'Ronald Weasley'),\n", + " Text(2, 0, 'Hermione Granger'),\n", + " Text(3, 0, 'Rubeus Hagrid'),\n", + " Text(4, 0, 'Severus Snape'),\n", + " Text(5, 0, 'Dudley Dursley'),\n", + " Text(6, 0, 'Draco Malfoy'),\n", + " Text(7, 0, 'Vernon Dursley'),\n", + " Text(8, 0, 'Albus Dumbledore'),\n", + " Text(9, 0, 'Neville Longbottom')])" ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "source": [ - "The `stream` mode of any algorithm in the GDS library returns a stream of records. Python client then automatically converts the output into a Pandas DataFrame.\n", - "\n", - "If you have ever executed the `stream` mode of the graph algorithms in Neo4j GDS library, you might be aware that the result contains internal node ids as a reference to nodes instead of actual node objects. The `pagerank_df` DataFrame contains two columns:\n", - "* nodeId: Internal node ids used to reference nodes\n", - "* score: PageRank score\n", - "\n", - "We can retrieve the referenced node objects using the `nodeId` column without constructing a Cypher statement by using the `gds.util.asNodes()` method. The `gds.util.asNodes()` method takes a list of internal node ids as input and outputs a list of node objects." - ], - "metadata": { - "id": "EuKnJ7Yzacb-" - } - }, - { - "cell_type": "code", - "source": [ - "# If you need to fetch information about node objects based on their internal node ids, you can use gds.util.asNodes\n", - "pagerank_df['node_object'] = gds.util.asNodes(pagerank_df['nodeId'].to_list())" - ], - "metadata": { - "id": "xTl4i5cPWbrh" - }, - "execution_count": 12, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "The `node_object` column now contains the referenced node objects. Node objects are defined in the underlying Neo4j Python driver. You can reference the [official documentation if you want to examine all the possible methods of the node object](https://neo4j.com/docs/api/python-driver/current/api.html#node).\n", - "\n", - "In this example, we will extract the `name` property from node objects and then visualize a bar chart of the top ten characters with the highest PageRank score." - ], - "metadata": { - "id": "fa-iw0etaubY" - } - }, - { - "cell_type": "code", - "source": [ - "# Extract name properties from the node object\n", - "pagerank_df['name'] = [n['name'] for n in pagerank_df['node_object']]\n", - "# Draw a bar chart\n", - "plt.figure(figsize=(16,9))\n", - "sns.barplot(x='name', y='score', data=pagerank_df.sort_values(by='score', ascending=False).head(10))\n", - "plt.xticks(rotation=45)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 652 - }, - "id": "6drUBiMBWqpJ", - "outputId": "9e3c906f-7d87-4404-d231-36f6fc0639cf" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n", - " )" - ] - }, - "metadata": {}, - "execution_count": 13 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Extract name properties from the node object\n", + "pagerank_df['name'] = [n['name'] for n in pagerank_df['node_object']]\n", + "# Draw a bar chart\n", + "plt.figure(figsize=(16,9))\n", + "sns.barplot(x='name', y='score', data=pagerank_df.sort_values(by='score', ascending=False).head(10))\n", + "plt.xticks(rotation=45)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7e7Jauk_a66I" + }, + "source": [ + "An additional benefit of having the graph algorithm output available in the Pandas Dataframe is that if you are not experienced with Cypher aggregations, you can simply skip them and do your aggregations in Pandas.\n", + "\n", + "As opposed to the `stream` mode of algorithms, the `stats`, `mutate`, and `write` modes do not produce a stream of results. Therefore, the results of Python client methods are not Pandas DataFrame. Instead, those methods output the algorithm metadata in Pandas Series format.\n", + "\n", + "For example, let's say we want to execute the mutate mode of the Louvain algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "mz5ARGwjEOPV", + "outputId": "a01ee5f2-da08-40b8-cdaa-e0f3ffdf8ed8" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "An additional benefit of having the graph algorithm output available in the Pandas Dataframe is that if you are not experienced with Cypher aggregations, you can simply skip them and do your aggregations in Pandas.\n", - "\n", - "As opposed to the `stream` mode of algorithms, the `stats`, `mutate`, and `write` modes do not produce a stream of results. Therefore, the results of Python client methods are not Pandas DataFrame. Instead, those methods output the algorithm metadata in Pandas Series format.\n", - "\n", - "For example, let's say we want to execute the mutate mode of the Louvain algorithm." - ], - "metadata": { - "id": "7e7Jauk_a66I" - } - }, - { - "cell_type": "code", - "source": [ - "# Louvain mutate\n", - "louvain_metadata = gds.louvain.mutate(G, mutateProperty='communityId', relationshipWeightProperty='weight')\n", - "print(louvain_metadata)" - ], - "metadata": { - "id": "mz5ARGwjEOPV", - "outputId": "a01ee5f2-da08-40b8-cdaa-e0f3ffdf8ed8", - "colab": { - "base_uri": "https://localhost:8080/" - } + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c91387e23a5e4b8caa670dd9c10373dd", + "version_major": 2, + "version_minor": 0 }, - "execution_count": 14, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "mutateMillis 0\n", - "nodePropertiesWritten 119\n", - "modularity 0.176974\n", - "modularities [0.15405649883268652, 0.17697414179849225]\n", - "ranLevels 2\n", - "communityCount 10\n", - "communityDistribution {'p99': 42, 'min': 2, 'max': 42, 'mean': 11.9,...\n", - "postProcessingMillis 4\n", - "preProcessingMillis 0\n", - "computeMillis 1257\n", - "configuration {'maxIterations': 10, 'seedProperty': None, 'c...\n", - "Name: 0, dtype: object\n" - ] - } + "text/plain": [ + "Louvain: 0%| | 0/100 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 17 - } + "text/plain": [ + " communityId communitySize\n", + "2 14 42\n", + "8 103 18\n", + "7 99 16\n", + "5 69 11\n", + "3 31 8\n", + "1 4 7\n", + "4 48 6\n", + "6 81 5\n", + "9 117 4\n", + "0 3 2" ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Rename columns\n", + "louvain_df.columns = ['nodeId', 'communityId']\n", + "# You can do all sorts of pandas operations like aggregations\n", + "louvain_df.groupby(\"communityId\").size().to_frame(\n", + " \"communitySize\"\n", + ").reset_index().sort_values(by=[\"communitySize\"], ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SbYFdS47bolR" + }, + "source": [ + "Like mentioned before, the added benefit of dealing with Pandas DataFrames as algorithm output is that you can apply all your Python skills to transform or manipulate the results. In this example, we simply grouped the DataFrame by the `communityId` column and count the members of each community.\n", + "\n", + "# Helpful methods\n", + "In the last part of this post, we will go over some of the helpful methods. The first one that comes to mind is listing all of the already projected in-memory graph with the `gds.graph.list()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 159 }, + "id": "GO4zQ6DKLCWG", + "outputId": "b94c92fc-7d02-46c0-84bc-f8263499827e" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "Like mentioned before, the added benefit of dealing with Pandas DataFrames as algorithm output is that you can apply all your Python skills to transform or manipulate the results. In this example, we simply grouped the DataFrame by the `communityId` column and count the members of each community.\n", - "\n", - "# Helpful methods\n", - "In the last part of this post, we will go over some of the helpful methods. The first one that comes to mind is listing all of the already projected in-memory graph with the `gds.graph.list()` method." - ], - "metadata": { - "id": "SbYFdS47bolR" - } - }, - { - "cell_type": "code", - "source": [ - "gds.graph.list()" + "data": { + "text/html": [ + "
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" ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gds.graph.list()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d92J_oYCbxxx" + }, + "source": [ + "Sometimes there are already projected in-memory graphs present in the database. If you don't have a reference to the projected graphs in the form of a Graph object, you cannot execute any graph algorithm. To avoid having to drop and recreate projected graphs, you can use the `gds.graph.get()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "UML8KJzWTpjN" + }, + "outputs": [], + "source": [ + "# G = gds.graph.get(\"graph name\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2hIU-lQ2b5xz" + }, + "source": [ + "When using the shortest path algorithms, you need to provide source and target nodes ids. You could use Cypher statements or you could use the `gds.find_node_id()` method.\n", + "\n", + "![find_node.drawio (1).png](data:image/png;base64,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)\n", + "\n", + "The `gds.find_node_id()` takes in two arguments. The first argument defines the node label we are searching for. In our example, we are searching for the `Character` node label. The second parameter specifies the node properties used to identify the particular node. The node properties are defined as a dictionary or map of key-value pairs, similar to the inline `MATCH` clause. The only difference is that we must add quotes around the key values of properties since otherwise, we would get a NameError in Python." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "2nTv7PcrTl7B", + "outputId": "a3dcfc07-b511-4701-fdfe-566f1b41ac31" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "Sometimes there are already projected in-memory graphs present in the database. If you don't have a reference to the projected graphs in the form of a Graph object, you cannot execute any graph algorithm. To avoid having to drop and recreate projected graphs, you can use the `gds.graph.get()` method." - ], - "metadata": { - "id": "d92J_oYCbxxx" - } - }, - { - "cell_type": "code", - "source": [ - "# G = gds.graph.get(\"graph name\")" - ], - "metadata": { - "id": "UML8KJzWTpjN" - }, - "execution_count": 19, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "When using the shortest path algorithms, you need to provide source and target nodes ids. You could use Cypher statements or you could use the `gds.find_node_id()` method.\n", - "\n", - "![find_node.drawio 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)\n", - "\n", - "The `gds.find_node_id()` takes in two arguments. The first argument defines the node label we are searching for. In our example, we are searching for the `Character` node label. The second parameter specifies the node properties used to identify the particular node. The node properties are defined as a dictionary or map of key-value pairs, similar to the inline `MATCH` clause. The only difference is that we must add quotes around the key values of properties since otherwise, we would get a NameError in Python." - ], - "metadata": { - "id": "2hIU-lQ2b5xz" - } - }, - { - "cell_type": "code", - "source": [ - "gds.find_node_id([\"Character\"], {\"name\":\"Harry Potter\"})" - ], - "metadata": { - "id": "2nTv7PcrTl7B", - "outputId": "a3dcfc07-b511-4701-fdfe-566f1b41ac31", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 20, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "4" - ] - }, - "metadata": {}, - "execution_count": 20 - } + "data": { + "text/plain": [ + "4" ] - }, - { - "cell_type": "markdown", - "source": [ - "The last useful method I will present here is the `drop()` method of a Graph object. It is used to release the projected graph from memory." - ], - "metadata": { - "id": "zj3XAI8McJdK" - } - }, - { - "cell_type": "code", - "source": [ - "# Drop a projected in-memory graph\n", - "G.drop()" - ], - "metadata": { - "id": "V23wjoyfKtag" - }, - "execution_count": 21, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# Conclusion\n", - "The Neo4j Graph Data Science Python client is designed to help you integrate Neo4j and its graph algorithms into your Python analytical workflows. The syntax of the Python client mimics the GDS Cypher procedures. Since not all graph algorithms are documented to be used as Python client method, you need to take into account the following guidelines when translating a Cypher procedure to a Python client method:\n", - "* When specifying a map or a dictionary as a parameter to any method, make sure to add quotes around the keys\n", - "* Instead of referencing the projected graph by its name, you need to input the * Graph object as the first parameter of graph algorithms\n", - "* Algorithm specific configuration parameter can be specified using keyword arguments\n", - "* The stream mode of graph algorithms outputs a Pandas DataFrame\n", - "* Other algorithm modes like stats , write , and mutate output the metadata of the algorithm call as a Pandas Series\n", - "\n", - "I am very excited about the new Python client and will be definitely using it in my workflows. Try it out and if you have any feedback please report it to the [official GitHub repository of the Python client](https://github.com/neo4j/graph-data-science-client)." - ], - "metadata": { - "id": "n0u3CxSccPgi" - } - }, + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gds.find_node_id([\"Character\"], {\"name\":\"Harry Potter\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zj3XAI8McJdK" + }, + "source": [ + "The last useful method I will present here is the `drop()` method of a Graph object. It is used to release the projected graph from memory." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "V23wjoyfKtag" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "" - ], - "metadata": { - "id": "8-VFIcGAcYQg" - }, - "execution_count": 21, - "outputs": [] + "data": { + "text/plain": [ + "graphName hp-graph\n", + "database neo4j\n", + "memoryUsage \n", + "sizeInBytes -1\n", + "nodeCount 119\n", + "relationshipCount 812\n", + "configuration {'relationshipProjection': {'INTERACTS': {'ori...\n", + "density 0.057827\n", + "creationTime 2023-02-01T12:15:46.030702248+00:00\n", + "modificationTime 2023-02-01T12:16:01.522004066+00:00\n", + "schema {'graphProperties': {}, 'relationships': {'INT...\n", + "schemaWithOrientation {'graphProperties': {}, 'relationships': {'INT...\n", + "Name: 0, dtype: object" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" } - ] -} \ No newline at end of file + ], + "source": [ + "# Drop a projected in-memory graph\n", + "G.drop()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n0u3CxSccPgi" + }, + "source": [ + "# Conclusion\n", + "The Neo4j Graph Data Science Python client is designed to help you integrate Neo4j and its graph algorithms into your Python analytical workflows. The syntax of the Python client mimics the GDS Cypher procedures. Since not all graph algorithms are documented to be used as Python client method, you need to take into account the following guidelines when translating a Cypher procedure to a Python client method:\n", + "* When specifying a map or a dictionary as a parameter to any method, make sure to add quotes around the keys\n", + "* Instead of referencing the projected graph by its name, you need to input the * Graph object as the first parameter of graph algorithms\n", + "* Algorithm specific configuration parameter can be specified using keyword arguments\n", + "* The stream mode of graph algorithms outputs a Pandas DataFrame\n", + "* Other algorithm modes like stats , write , and mutate output the metadata of the algorithm call as a Pandas Series\n", + "\n", + "I am very excited about the new Python client and will be definitely using it in my workflows. Try it out and if you have any feedback please report it to the [official GitHub repository of the Python client](https://github.com/neo4j/graph-data-science-client)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "8-VFIcGAcYQg" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyMLODtjsTX2gWhXe5ADDUdP", + "include_colab_link": true, + "name": "gds-python.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/gds_python/p2p-network-analysis.ipynb b/gds_python/p2p-network-analysis.ipynb index 7f3fd73..25c7f72 100644 --- a/gds_python/p2p-network-analysis.ipynb +++ b/gds_python/p2p-network-analysis.ipynb @@ -39,9 +39,9 @@ "source": [ "## Environment setup\n", "We will be using Neo4j as the database to store the peer-to-peer network. Therefore, I suggest you download and install the Neo4j Desktop application if you want to follow along with the code examples.\n", - "The dataset is available as a [database dump](https://drive.google.com/file/d/1_N_QLtCRI-eeLzjEIFZAbj8YQrWfTolI/view). It is a variation of the database dump available on Neo4j's product example GitHub to showcase fraud detection.\n", + "The dataset is available as a [database dump](https://drive.google.com/file/d/1apR3xwWEOdi_WKmIAGk1bPqhHQSgxwT-/view?usp=share_link). It is a variation of the database dump available on Neo4j's product example GitHub to showcase fraud detection.\n", "\n", - "I've written a post about restoring a database dump in Neo4j Desktop sometime ago if you need some help. After you have restored the database dump, you will also need to install the Graph Data Science and APOC libraries. Make sure you are using version 2.1.0 of the GDS library or later.\n", + "I've written a post about restoring a database dump in Neo4j Desktop sometime ago if you need some help. After you have restored the database dump, you will also need to install the Graph Data Science and APOC libraries. Make sure you are using version 2.3.0 of the GDS library or later.\n", "\n", "You will need to have the following three Python libraries installed:\n", "* graphdatascience: Neo4j Graph Data Science Python client\n", @@ -85,14 +85,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.1.2\n" + "2.3.0\n" ] } ], "source": [ "host = \"bolt://localhost:7687\"\n", "user = \"neo4j\"\n", - "password = \"letmein\"\n", + "password = \"pleaseletmein\"\n", "\n", "gds = GraphDataScience(host, auth=(user, password))\n", "\n", @@ -119,8 +119,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[{'User': 33732, 'Device': 51451, 'IP': 585855, 'Node': 3, 'Card': 118818}]\n", - "[{'HAS_IP': 1488949, 'REFERRED': 1870, 'REL': 1, 'USED': 55026, 'HAS_CC': 128066, 'P2P': 102832}]\n" + "[{'User': 33732, 'Device': 51451, 'IP': 585855, 'Card': 118818}]\n", + "[{'HAS_IP': 1488949, 'REFERRED': 1870, 'USED': 55026, 'HAS_CC': 128066, 'P2P': 102832}]\n" ] } ], @@ -224,30 +224,16 @@ "id": "3d6fcef5", "metadata": {}, "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c5cf93aa3c5649cdac6470bcfc8c9a9c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Loading: 0%| | 0/100 [00:00" + "" ] }, "execution_count": 11, @@ -572,7 +558,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -631,7 +617,7 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -645,7 +631,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -655,7 +641,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -665,7 +651,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -685,7 +671,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -697,19 +683,19 @@ "" ], "text/plain": [ - "nodeProperty nodeId accountYears avgTransactionAmount betweenness \\\n", - "0 0 4.0 98.782609 24985.335938 \n", - "1 1 4.0 100.000000 0.000000 \n", - "2 3 4.0 122.000000 0.000000 \n", - "3 4 0.0 0.000000 0.000000 \n", - "4 14 4.0 368.000000 0.000000 \n", + " nodeId accountYears avgTransactionAmount betweenness closeness \\\n", + "0 0 5.0 98.782609 24985.335938 0.163444 \n", + "1 1 5.0 100.000000 0.000000 0.075315 \n", + "2 3 5.0 122.000000 0.000000 0.137684 \n", + "3 4 0.0 0.000000 0.000000 0.171180 \n", + "4 14 5.0 368.000000 0.000000 0.172998 \n", "\n", - "nodeProperty closeness weightedIndegree weightedOutdegree \n", - "0 0.163444 1000.00 2272.0 \n", - "1 0.075315 100.00 100.0 \n", - "2 0.137684 167.22 122.0 \n", - "3 0.171180 210.00 0.0 \n", - "4 0.172998 500.00 2576.0 " + " weightedIndegree weightedOutdegree \n", + "0 1000.00 2272.0 \n", + "1 100.00 100.0 \n", + "2 167.22 122.0 \n", + "3 210.00 0.0 \n", + "4 500.00 2576.0 " ] }, "execution_count": 12, @@ -767,7 +753,7 @@ "
nodePropertynodeIdaccountYearsavgTransactionAmount
004.05.098.78260924985.3359380.163444
114.05.0100.0000000.0000000.075315
234.05.0122.0000000.0000000.137684
4144.05.0368.0000000.0000000.172998
\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -791,7 +777,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -801,7 +787,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -841,7 +827,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -851,7 +837,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -863,25 +849,25 @@ "" ], "text/plain": [ - "nodeProperty nodeId accountYears avgTransactionAmount betweenness \\\n", - "count 11311.000000 11311.000000 11311.000000 1.131100e+04 \n", - "mean 16789.334542 2.216426 99.198514 1.967391e+04 \n", - "std 9749.632411 2.349186 331.473144 2.176740e+05 \n", - "min 0.000000 0.000000 0.000000 0.000000e+00 \n", - "25% 8308.000000 0.000000 0.000000 0.000000e+00 \n", - "50% 16756.000000 0.000000 0.000000 0.000000e+00 \n", - "75% 25289.000000 5.000000 54.687500 0.000000e+00 \n", - "max 33724.000000 5.000000 4000.000000 1.129122e+07 \n", + " nodeId accountYears avgTransactionAmount betweenness \\\n", + "count 11311.000000 11311.000000 11311.000000 1.131100e+04 \n", + "mean 16789.334542 2.693307 99.198514 1.967391e+04 \n", + "std 9749.632411 2.843814 331.473144 2.176740e+05 \n", + "min 0.000000 0.000000 0.000000 0.000000e+00 \n", + "25% 8308.000000 0.000000 0.000000 0.000000e+00 \n", + "50% 16756.000000 0.000000 0.000000 0.000000e+00 \n", + "75% 25289.000000 6.000000 54.687500 0.000000e+00 \n", + "max 33724.000000 6.000000 4000.000000 1.129122e+07 \n", "\n", - "nodeProperty closeness weightedIndegree weightedOutdegree \n", - "count 11311.000000 11311.000000 1.131100e+04 \n", - "mean 0.180162 1527.190995 1.527191e+03 \n", - "std 0.175071 13206.700185 1.637541e+04 \n", - "min 0.000000 0.000000 0.000000e+00 \n", - "25% 0.129951 5.000000 0.000000e+00 \n", - "50% 0.155281 30.000000 0.000000e+00 \n", - "75% 0.170334 300.000000 1.390000e+02 \n", - "max 1.000000 498911.090000 1.065990e+06 " + " closeness weightedIndegree weightedOutdegree \n", + "count 11311.000000 11311.000000 1.131100e+04 \n", + "mean 0.180162 1527.190995 1.527191e+03 \n", + "std 0.175071 13206.700185 1.637541e+04 \n", + "min 0.000000 0.000000 0.000000e+00 \n", + "25% 0.129951 5.000000 0.000000e+00 \n", + "50% 0.155281 30.000000 0.000000e+00 \n", + "75% 0.170334 300.000000 1.390000e+02 \n", + "max 1.000000 498911.090000 1.065990e+06 " ] }, "execution_count": 13, @@ -910,7 +896,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -919,7 +905,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -972,8 +958,8 @@ "mutateMillis 0\n", "postProcessingMillis 0\n", "preProcessingMillis 0\n", - "computeMillis 23\n", - "configuration {'jobId': '21d64bc4-f207-4331-886d-a8276ec15d0...\n", + "computeMillis 20\n", + "configuration {'jobId': '2cb069c4-39e3-444b-b674-a75a2b96ad5...\n", "Name: 0, dtype: object" ] }, @@ -1037,45 +1023,57 @@ "
\n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", "
nodePropertynodeIdaccountYearsavgTransactionAmount
mean16789.3345422.2164262.69330799.1985141.967391e+040.180162
std9749.6324112.3491862.843814331.4731442.176740e+050.175071
75%25289.0000005.0000006.00000054.6875000.000000e+000.170334
max33724.0000005.0000006.0000004000.0000001.129122e+071.000000nodeIdcommunityIddistanceFromCentroidsilhouette
020000020.005067-1.0
120011201.148309-1.0
220014020.026275-1.0
320017350.087512-1.0
42002610.225664-1.0
\n", "" ], "text/plain": [ - " nodeId communityId\n", - "0 20000 0\n", - "1 20011 2\n", - "2 20014 0\n", - "3 20017 3\n", - "4 20026 1" + " nodeId communityId distanceFromCentroid silhouette\n", + "0 20000 2 0.005067 -1.0\n", + "1 20011 0 1.148309 -1.0\n", + "2 20014 2 0.026275 -1.0\n", + "3 20017 5 0.087512 -1.0\n", + "4 20026 1 0.225664 -1.0" ] }, "execution_count": 16, @@ -1084,7 +1082,7 @@ } ], "source": [ - "kmeans_df = gds.alpha.kmeans.stream(\n", + "kmeans_df = gds.beta.kmeans.stream(\n", " largestComponentGraph, nodeProperty=\"features\", k=6, randomSeed=42\n", ")\n", "kmeans_df.head()" @@ -1134,32 +1132,32 @@ " \n", " 0\n", " 0\n", - " 3225\n", + " 375\n", " \n", " \n", " 1\n", " 1\n", - " 4870\n", + " 4792\n", " \n", " \n", " 2\n", " 2\n", - " 570\n", + " 3637\n", " \n", " \n", " 3\n", " 3\n", - " 1961\n", + " 348\n", " \n", " \n", " 4\n", " 4\n", - " 217\n", + " 236\n", " \n", " \n", " 5\n", " 5\n", - " 468\n", + " 1923\n", " \n", " \n", "\n", @@ -1167,12 +1165,12 @@ ], "text/plain": [ " communityId communitySize\n", - "0 0 3225\n", - "1 1 4870\n", - "2 2 570\n", - "3 3 1961\n", - "4 4 217\n", - "5 5 468" + "0 0 375\n", + "1 1 4792\n", + "2 2 3637\n", + "3 3 348\n", + "4 4 236\n", + "5 5 1923" ] }, "execution_count": 17, @@ -1205,7 +1203,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 18, @@ -1214,7 +1212,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1371,17 +1369,18 @@ { "data": { "text/plain": [ - "graphName largestComponent\n", - "database neo4j\n", - "memoryUsage \n", - "sizeInBytes -1\n", - "nodeCount 11311\n", - "relationshipCount 52272\n", - "configuration {'jobId': '3ea5e089-5118-4f2c-9222-f8710cb8770...\n", - "density 0.000409\n", - "creationTime 2022-06-28T11:39:18.691439000+02:00\n", - "modificationTime 2022-06-28T11:40:06.085760000+02:00\n", - "schema {'graphProperties': {}, 'relationships': {'P2P...\n", + "graphName largestComponent\n", + "database neo4j\n", + "memoryUsage \n", + "sizeInBytes -1\n", + "nodeCount 11311\n", + "relationshipCount 52272\n", + "configuration {'jobId': '36a5f9cd-005a-41d5-838f-6cb665d09f8...\n", + "density 0.000409\n", + "creationTime 2023-02-01T13:18:11.499292052+01:00\n", + "modificationTime 2023-02-01T13:19:23.300208879+01:00\n", + "schema {'graphProperties': {}, 'relationships': {'P2P...\n", + "schemaWithOrientation {'graphProperties': {}, 'relationships': {'P2P...\n", "Name: 0, dtype: object" ] }, diff --git a/harry_potter/Harry_Potter_Karate_Club_integration.ipynb b/harry_potter/Harry_Potter_Karate_Club_integration.ipynb index 7f3f704..fd4c0f0 100644 --- a/harry_potter/Harry_Potter_Karate_Club_integration.ipynb +++ b/harry_potter/Harry_Potter_Karate_Club_integration.ipynb @@ -1,2323 +1,1469 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-aWuO6deuj4U" + }, + "source": [ + "* Updated to GDS 2.0 version\n", + "* Link to original blog post: https://towardsdatascience.com/integrate-neo4j-with-karateclub-node-embedding-package-99715d73250a" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { "colab": { - "name": "Harry Potter - Karate Club integration.ipynb", - "provenance": [], - "authorship_tag": "ABX9TyOelByQliYNdEZtLAG5ICeE", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "base_uri": "https://localhost:8080/" }, - "language_info": { - "name": "python" + "id": "Ln3_AG0uiTiW", + "outputId": "f78e1075-d615-4570-ddfb-395337274495" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n", + "Collecting neo4j\n", + " Downloading neo4j-4.4.2.tar.gz (89 kB)\n", + "\u001b[K |████████████████████████████████| 89 kB 3.8 MB/s \n", + "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (1.0.2)\n", + "Collecting karateclub\n", + " Downloading karateclub-1.2.3.tar.gz (62 kB)\n", + "\u001b[K |████████████████████████████████| 62 kB 548 kB/s \n", + "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2018.9)\n", + "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (1.21.5)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n", + "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.4.1)\n", + "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.1.0)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (3.1.0)\n", + "Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from karateclub) (2.6.3)\n", + "Requirement already satisfied: decorator==4.4.2 in /usr/local/lib/python3.7/dist-packages (from karateclub) (4.4.2)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from karateclub) (4.64.0)\n", + "Requirement already satisfied: python-louvain in /usr/local/lib/python3.7/dist-packages (from karateclub) (0.16)\n", + "Collecting pygsp\n", + " Downloading PyGSP-0.5.1-py2.py3-none-any.whl (1.8 MB)\n", + "\u001b[K |████████████████████████████████| 1.8 MB 7.8 MB/s \n", + "\u001b[?25hCollecting gensim>=4.0.0\n", + " Downloading gensim-4.1.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (24.1 MB)\n", + "\u001b[K |████████████████████████████████| 24.1 MB 2.2 MB/s \n", + "\u001b[?25hCollecting python-Levenshtein\n", + " Downloading python-Levenshtein-0.12.2.tar.gz (50 kB)\n", + "\u001b[K |████████████████████████████████| 50 kB 2.8 MB/s \n", + "\u001b[?25hRequirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.7/dist-packages (from gensim>=4.0.0->karateclub) (5.2.1)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from python-Levenshtein->karateclub) (57.4.0)\n", + "Building wheels for collected packages: neo4j, karateclub, python-Levenshtein\n", + " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for neo4j: filename=neo4j-4.4.2-py3-none-any.whl size=115365 sha256=4941018e1ed8fd95da841b0f878d0b0b5fe7830d6ac387f5d42f17d2106a054f\n", + " Stored in directory: /root/.cache/pip/wheels/10/d6/28/95029d7f69690dbc3b93e4933197357987de34fbd44b50a0e4\n", + " Building wheel for karateclub (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for karateclub: filename=karateclub-1.2.3-py3-none-any.whl size=97754 sha256=54c83ec56a4864ca4fc21b2856d5f7a4bcd86c3e0515fb8abe411b86176f8c1d\n", + " Stored in directory: /root/.cache/pip/wheels/7a/09/80/0d50455fd4e297e88f8f38a711c6f4849e6bd1a330000dde3d\n", + " Building wheel for python-Levenshtein (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp37-cp37m-linux_x86_64.whl size=149868 sha256=2ecbfbdcde0c31ee08f23a76147cc73a61fe17841cfe478691b3ab8124c62e24\n", + " Stored in directory: /root/.cache/pip/wheels/05/5f/ca/7c4367734892581bb5ff896f15027a932c551080b2abd3e00d\n", + "Successfully built neo4j karateclub python-Levenshtein\n", + "Installing collected packages: python-Levenshtein, pygsp, gensim, neo4j, karateclub\n", + " Attempting uninstall: gensim\n", + " Found existing installation: gensim 3.6.0\n", + " Uninstalling gensim-3.6.0:\n", + " Successfully uninstalled gensim-3.6.0\n", + "Successfully installed gensim-4.1.2 karateclub-1.2.3 neo4j-4.4.2 pygsp-0.5.1 python-Levenshtein-0.12.2\n" + ] } + ], + "source": [ + "!pip install pandas neo4j scikit-learn karateclub" + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "* Updated to GDS 2.0 version\n", - "* Link to original blog post: https://towardsdatascience.com/integrate-neo4j-with-karateclub-node-embedding-package-99715d73250a" - ], - "metadata": { - "id": "-aWuO6deuj4U" - } - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Ln3_AG0uiTiW", - "outputId": "f78e1075-d615-4570-ddfb-395337274495" - }, - "source": [ - "!pip install pandas neo4j scikit-learn karateclub" - ], - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n", - "Collecting neo4j\n", - " Downloading neo4j-4.4.2.tar.gz (89 kB)\n", - "\u001b[K |████████████████████████████████| 89 kB 3.8 MB/s \n", - "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (1.0.2)\n", - "Collecting karateclub\n", - " Downloading karateclub-1.2.3.tar.gz (62 kB)\n", - "\u001b[K |████████████████████████████████| 62 kB 548 kB/s \n", - "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n", - "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2018.9)\n", - "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (1.21.5)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n", - "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.4.1)\n", - "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.1.0)\n", - "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (3.1.0)\n", - "Requirement already satisfied: networkx in 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satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.7/dist-packages (from gensim>=4.0.0->karateclub) (5.2.1)\n", - "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from python-Levenshtein->karateclub) (57.4.0)\n", - "Building wheels for collected packages: neo4j, karateclub, python-Levenshtein\n", - " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for neo4j: filename=neo4j-4.4.2-py3-none-any.whl size=115365 sha256=4941018e1ed8fd95da841b0f878d0b0b5fe7830d6ac387f5d42f17d2106a054f\n", - " Stored in directory: /root/.cache/pip/wheels/10/d6/28/95029d7f69690dbc3b93e4933197357987de34fbd44b50a0e4\n", - " Building wheel for karateclub (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for karateclub: filename=karateclub-1.2.3-py3-none-any.whl size=97754 sha256=54c83ec56a4864ca4fc21b2856d5f7a4bcd86c3e0515fb8abe411b86176f8c1d\n", - " Stored in directory: /root/.cache/pip/wheels/7a/09/80/0d50455fd4e297e88f8f38a711c6f4849e6bd1a330000dde3d\n", - " Building wheel for python-Levenshtein (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp37-cp37m-linux_x86_64.whl size=149868 sha256=2ecbfbdcde0c31ee08f23a76147cc73a61fe17841cfe478691b3ab8124c62e24\n", - " Stored in directory: /root/.cache/pip/wheels/05/5f/ca/7c4367734892581bb5ff896f15027a932c551080b2abd3e00d\n", - "Successfully built neo4j karateclub python-Levenshtein\n", - "Installing collected packages: python-Levenshtein, pygsp, gensim, neo4j, karateclub\n", - " Attempting uninstall: gensim\n", - " Found existing installation: gensim 3.6.0\n", - " Uninstalling gensim-3.6.0:\n", - " Successfully uninstalled gensim-3.6.0\n", - "Successfully installed gensim-4.1.2 karateclub-1.2.3 neo4j-4.4.2 pygsp-0.5.1 python-Levenshtein-0.12.2\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "pD8HtmZxidNG" - }, - "source": [ - "import networkx as nx\n", - "import pandas as pd\n", - "from neo4j import GraphDatabase\n", - "# Change the host and user/password combination to your neo4j\n", - "host = 'bolt://3.235.2.228:7687'\n", - "user = 'neo4j'\n", - "password = 'seats-drunks-carbon'\n", - "driver = GraphDatabase.driver(host,auth=(user, password))\n", - "\n", - "def read_query(query, params=None):\n", - " with driver.session() as session:\n", - " result = session.run(query, params)\n", - " return pd.DataFrame([r.values() for r in result], columns=result.keys())" - ], - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KZr8Abs_b1hl" - }, - "source": [ - "Lately, I have been on a quest to learn as much as possible about node embedding techniques. The goal of node embedding is to encode nodes so that the similarity in the embedding space approximates similarity in the original network. In layman’s terms, we encode each node to a fixed size vector that preserves the similarity of the original network.\n", - "\n", - "I have come across the [Karate Club](https://github.com/benedekrozemberczki/karateclub) package in my search for the implementation of various node embedding models. I will let the author Benedek Rozemberczki explain what its purpose is:\n", - ">Karate Club consists of state-of-the-art methods to do unsupervised learning on graph structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. First, it provides network embedding techniques at the node and graph level. Second, it includes a variety of overlapping and non-overlapping community detection methods. Implemented methods cover a wide range of network science (NetSci, Complenet), data mining (ICDM, CIKM, KDD), artificial intelligence (AAAI, IJCAI) and machine learning (NeurIPS, ICML, ICLR) conferences, workshops, and pieces from prominent journals.\n", - "\n", - "The Karate Club project features:\n", - "10+ community detection models\n", - "25+ node embedding models\n", - "10+ graph embedding models\n", - "As you might know, I like to store my network information in Neo4j. In this blog post, I will demonstrate how to extract network information from Neo4j and use it as an input to the Karate Club API. It is a straightforward transformation. We have to transform a Neo4j graph to a NetworkX graph model, as Karate Club uses NetworkX structure, and we are good to go. \n", - "\n", - "#Data model\n", - "We will use a simple toy graph of the Harry Potter universe that I have created in my previous blog post. I have prepared a CSV file with the network structure, so you don’t have to complete the NLP process yourself.\n", - "\n", - "The network is based on the Harry Potter and the Sorcerer’s Stone book. Nodes represent the character in the book, and the INTERACTS relationships represent co-occurrences in the text between characters. To import this network, execute the following Cypher query:\n", - "P.s. If you are following along with the Colab notebook, I suggest you open a blank [Neo4j Sandbox project](https://neo4j.com/sandbox/)." - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - }, - "id": "EDYtEnBPikiu", - "outputId": "7d658614-b8ed-42a7-a709-585a497461d0" - }, - "source": [ - "# import data\n", - "read_query(\"\"\"\n", - "LOAD CSV WITH HEADERS FROM \"https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/HP/hp_1.csv\" as row\n", - "MERGE (s:Character{name:row.source})\n", - "MERGE (t:Character{name:row.target})\n", - "MERGE (s)-[r:INTERACTS]-(t)\n", - "SET r.weight = row.weight\n", - "RETURN distinct 'import successful' as result\n", - "\"\"\")" - ], - "execution_count": 3, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " result\n", - "0 import successful" - ], - "text/html": [ - "\n", - "
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01851190.345871[0.28369409594991385, 0.34587104758669224]27{'p99': 29, 'min': 8, 'max': 29, 'mean': 17.0,...301769{'maxIterations': 10, 'writeConcurrency': 4, '...
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writeMillisnodePropertiesWrittenmodularitymodularitiesranLevelscommunityCountcommunityDistributionpostProcessingMillispreProcessingMilliscomputeMillisconfiguration
0311190.348028[0.28802264068528716, 0.3480277366594676]27{'p99': 29, 'min': 8, 'max': 29, 'mean': 17.0,...303485{'maxIterations': 10, 'writeConcurrency': 4, '...
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" ], - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RExm6VXYdOa9" - }, - "source": [ - "Now, we must export the relevant network data and construct a NetworkX graph model out of it. NetworkX graph can be constructed by only providing the edge list. The syntax of the edge list is:\n", - "```\n", - "[\"1 2 {'weight': 3}\", \"2 3 {'weight': 27}\", \"3 4 {'weight': 3.0}\"]\n", - "```\n", - "Now, we can go ahead and construct a NetworkX graph model of the Harry Potter universe." + "text/plain": [ + " writeMillis nodePropertiesWritten modularity \\\n", + "0 31 119 0.348028 \n", + "\n", + " modularities ranLevels communityCount \\\n", + "0 [0.28802264068528716, 0.3480277366594676] 2 7 \n", + "\n", + " communityDistribution postProcessingMillis \\\n", + "0 {'p99': 29, 'min': 8, 'max': 29, 'mean': 17.0,... 3 \n", + "\n", + " preProcessingMillis computeMillis \\\n", + "0 0 3485 \n", + "\n", + " configuration \n", + "0 {'maxIterations': 10, 'writeConcurrency': 4, '... " ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "read_query(\"\"\"\n", + "CALL gds.louvain.write('got',{\n", + " writeProperty:'louvain'\n", + "})\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i0iCbJxSdC3I" + }, + "source": [ + "A critical detail of the network analysis is that the interaction network between the characters is undirected. I won’t go into the algorithms’ theory or their pros and cons. The goal of this blog post is purely to help you get started with integrating Karate Club and Neo4j. The ideas and differentiation of the algorithms may come in another blog post. Now let’s run some algorithms in KC. KC only works when nodes in the graph have consecutive ids. I don’t know the reason behind this choice; that’s just how it is. We can easily create a mapping to consecutive ids and store it in Neo4j." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "N4mijxU8i7zw", + "outputId": "f7621460-00a1-48ff-ed1c-96db8c64a6da" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 319 - }, - "id": "kDXnmI6cjNrh", - "outputId": "6fd9e026-50ca-414b-b3d4-311c64df4484" - }, - "source": [ - "# Construct a networkX graph\n", - "edge_list = read_query(\"\"\"\n", - "MATCH (s:Character)-[r:INTERACTS]->(t:Character)\n", - "WITH toString(s.index) + \" \" + toString(t.index) + \" {'weight':\" + toString(r.weight) + \"}\" as edge\n", - "WITH collect(edge) as result\n", - "RETURN result\n", - "\"\"\")\n", - "\n", - "edge_list = edge_list['result'].to_list()[0]\n", - "G = nx.parse_edgelist(edge_list, create_using=nx.Graph(), nodetype=int)\n", - "nx.draw(G)" + "data": { + "text/html": [ + "
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- }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DkOH7iG1deLW" - }, - "source": [ - "Take special care to the create_using parameter. In this case, I wanted to define an undirected graph, so I have used the nx.Graph option. If you are dealing with a directed graph or even a multigraph, choose the according create_using parameter.\n", - "\n", - "Now that we have constructed the NetworkX graph, we can go ahead and test KC algorithms. We will begin with a community detection algorithm BigClam. We will calculate the community structure and write the results back to Neo4j." + "text/plain": [ + " result\n", + "0 done" ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# KarateClub only works on nodes with consecutive ids\n", + "read_query(\"\"\"\n", + "MATCH (c:Character)\n", + "WITH count(*) as number, collect(c) as nodes\n", + "UNWIND range(0, number - 1) as index\n", + "WITH nodes[index] as node, index\n", + "SET node.index = index\n", + "RETURN distinct 'done' as result\n", + "\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "uXfnlMoJja-S" + }, + "outputs": [], + "source": [ + "# Define character mapping\n", + "character_mapping = read_query(\"\"\"\n", + "MATCH (c:Character)\n", + "WHERE exists { (c)-[:INTERACTS]-() }\n", + "RETURN c.name as character, c.index as index\n", + "ORDER BY index\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RExm6VXYdOa9" + }, + "source": [ + "Now, we must export the relevant network data and construct a NetworkX graph model out of it. NetworkX graph can be constructed by only providing the edge list. The syntax of the edge list is:\n", + "```\n", + "[\"1 2 {'weight': 3}\", \"2 3 {'weight': 27}\", \"3 4 {'weight': 3.0}\"]\n", + "```\n", + "Now, we can go ahead and construct a NetworkX graph model of the Harry Potter universe." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 319 }, + "id": "kDXnmI6cjNrh", + "outputId": "6fd9e026-50ca-414b-b3d4-311c64df4484" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "K-q8oWR6jgPC" - }, - "source": [ - "from karateclub.community_detection.overlapping import BigClam\n", - "\n", - "model = BigClam()\n", - "model.fit(G)\n", - "results = model.get_memberships()" - ], - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - }, - "id": "N5adb__ijyqB", - "outputId": "32fa2a9b-68e9-474c-e598-45d321fe5826" - }, - "source": [ - "data = [{'index': int(el), 'value': int(results[el])} for el in results]\n", - "read_query(\"\"\"\n", - "UNWIND $data as row\n", - "MATCH (c:Character{index:row.index})\n", - "SET c.bigClam = row.value\n", - "RETURN distinct 'done' as result\n", - "\"\"\", {'data':data})" - ], - "execution_count": 10, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " result\n", - "0 done" - ], - "text/html": [ - "\n", - "
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" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Construct a networkX graph\n", + "edge_list = read_query(\"\"\"\n", + "MATCH (s:Character)-[r:INTERACTS]->(t:Character)\n", + "WITH toString(s.index) + \" \" + toString(t.index) + \" {'weight':\" + toString(r.weight) + \"}\" as edge\n", + "WITH collect(edge) as result\n", + "RETURN result\n", + "\"\"\")\n", + "\n", + "edge_list = edge_list['result'].to_list()[0]\n", + "G = nx.parse_edgelist(edge_list, create_using=nx.Graph(), nodetype=int)\n", + "nx.draw(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DkOH7iG1deLW" + }, + "source": [ + "Take special care to the create_using parameter. In this case, I wanted to define an undirected graph, so I have used the nx.Graph option. If you are dealing with a directed graph or even a multigraph, choose the according create_using parameter.\n", + "\n", + "Now that we have constructed the NetworkX graph, we can go ahead and test KC algorithms. We will begin with a community detection algorithm BigClam. We will calculate the community structure and write the results back to Neo4j." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "K-q8oWR6jgPC" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "2OyJcLrydoer" - }, - "source": [ - "KC API is very simple to use. You just define the desired graph algorithm and input the NetworkX graph model in the fit method and that’s it. Couldn’t be simpler than that.\n", - "\n", - "# Node embeddings\n", - "Again, the Neo4j GDS library provides node embedding algorithms like FastRP, node2vec, and GraphSAGE. I will show the syntax for FastRP algorithm, but again, won’t delve much into hyper-parameter optimization." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/tomaz/anaconda3/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "from karateclub.community_detection.overlapping import BigClam\n", + "\n", + "model = BigClam()\n", + "model.fit(G)\n", + "results = model.get_memberships()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "N5adb__ijyqB", + "outputId": "32fa2a9b-68e9-474c-e598-45d321fe5826" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - }, - "id": "G6WJX3-kduSv", - "outputId": "08e9873f-5d07-467e-e96d-3973d779c745" - }, - "source": [ - "read_query(\"\"\"\n", - "CALL gds.fastRP.write('got',{\n", - " embeddingDimension: 64,\n", - " writeProperty: 'fastrp'})\n", - "\"\"\")" + "data": { + "text/html": [ + "
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" ], - "execution_count": 12, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CVj3tIdwd4cd" - }, - "source": [ - "We will begin with the NetMF algorithm. NetMF algorithm fall into the community-based node embedding category. If you want to learn more about the technical details, read the original paper or examine the code." + "text/plain": [ + " nodeCount nodePropertiesWritten preProcessingMillis computeMillis \\\n", + "0 119 119 0 100 \n", + "\n", + " writeMillis configuration \n", + "0 106 {'writeConcurrency': 4, 'nodeSelfInfluence': 0... " ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "read_query(\"\"\"\n", + "CALL gds.fastRP.write('got',{\n", + " embeddingDimension: 64,\n", + " writeProperty: 'fastrp'})\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MfyRm-_3dyCi" + }, + "source": [ + "The embeddingDimension parameter is mandatory and defines the size of the embedding vector for each node. Other than that, we have again defined the interaction network to be treated as undirected.\n", + "\n", + "Now let’s try out some of the node embedding algorithms in the KC package. First, we will define a function that will draw a t-SNE scatter plot of embedding results." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "mw9VEBndmVE8" + }, + "outputs": [], + "source": [ + "from sklearn.manifold import TSNE\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "def tsne(embeddings, hue=None):\n", + " tsne = TSNE(n_components=2, n_iter=300)\n", + " tsne_results = tsne.fit_transform(embeddings['embedding'].to_list())\n", + "\n", + " embeddings['tsne_x'] = [x[0] for x in list(tsne_results)]\n", + " embeddings['tsne_y'] = [x[1] for x in list(tsne_results)]\n", + "\n", + " plt.figure(figsize=(18,10))\n", + " sns.scatterplot(\n", + " x=\"tsne_x\", y=\"tsne_y\",\n", + " hue=hue,\n", + " palette=sns.color_palette(\"hls\", 10),\n", + " data=embeddings,\n", + " legend=\"full\",\n", + " alpha=0.9\n", + " )\n", + " \n", + " for i in range(df.shape[0]):\n", + " plt.text(x=df['tsne_x'][i]+0.3,y=df['tsne_y'][i]+0.3,s=df.character[i], \n", + " fontdict=dict(color='black',size=10),)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CVj3tIdwd4cd" + }, + "source": [ + "We will begin with the NetMF algorithm. NetMF algorithm fall into the community-based node embedding category. If you want to learn more about the technical details, read the original paper or examine the code." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 675 }, + "id": "-igmAtnql7OZ", + "outputId": "30c30679-c69a-4a25-bfd6-882aa9de1afa" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 675 - }, - "id": "-igmAtnql7OZ", - "outputId": "30c30679-c69a-4a25-bfd6-882aa9de1afa" - }, - "source": [ - "from karateclub.node_embedding.neighbourhood import NetMF\n", - "\n", - "\"\"\"\n", - "dimensions (int): Number of embedding dimension. Default is 32.\n", - "iteration (int): Number of SVD iterations. Default is 10.\n", - "order (int): Number of PMI matrix powers. Default is 2.\n", - "negative_samples (in): Number of negative samples. Default is 1.\n", - "seed (int): SVD random seed. Default is 42.\n", - "\"\"\"\n", - "\n", - "model = NetMF(dimensions=64)\n", - "model.fit(G)\n", - "embedding = model.get_embedding()\n", - "\n", - "results = []\n", - "for name,embedding in zip(character_mapping['character'].to_list(), embedding):\n", - " results.append({'character': name, 'embedding': embedding}) \n", - "df = pd.DataFrame.from_dict(results)\n", - "tsne(df)" - ], - "execution_count": 13, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n", - " FutureWarning,\n", - "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n", - " FutureWarning,\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/tomaz/.local/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:795: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n", + " warnings.warn(\n", + "/home/tomaz/.local/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:805: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "AvPxkrdGd8hG" - }, - "source": [ - "The KC library also features the NEU algorithm. The procedure uses an arbitrary embedding and augments it by higher order proximities with a recursive meta learning algorithm." + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from karateclub.node_embedding.neighbourhood import NetMF\n", + "\n", + "\"\"\"\n", + "dimensions (int): Number of embedding dimension. Default is 32.\n", + "iteration (int): Number of SVD iterations. Default is 10.\n", + "order (int): Number of PMI matrix powers. Default is 2.\n", + "negative_samples (in): Number of negative samples. Default is 1.\n", + "seed (int): SVD random seed. Default is 42.\n", + "\"\"\"\n", + "\n", + "model = NetMF(dimensions=64)\n", + "model.fit(G)\n", + "embedding = model.get_embedding()\n", + "\n", + "results = []\n", + "for name,embedding in zip(character_mapping['character'].to_list(), embedding):\n", + " results.append({'character': name, 'embedding': embedding}) \n", + "df = pd.DataFrame.from_dict(results)\n", + "tsne(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AvPxkrdGd8hG" + }, + "source": [ + "The KC library also features the NEU algorithm. The procedure uses an arbitrary embedding and augments it by higher order proximities with a recursive meta learning algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 606 }, + "id": "m-bOrtHMmhVd", + "outputId": "56128b10-1d2b-447e-d395-c968c7257776" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 606 - }, - "id": "m-bOrtHMmhVd", - "outputId": "56128b10-1d2b-447e-d395-c968c7257776" - }, - "source": [ - "from karateclub.node_embedding.meta import NEU\n", - "\n", - "\"\"\"\n", - "L1 (float): Weight of lower order proximities. Defauls is 0.5\n", - "L2 (float): Weight of higer order proximities. Default is 0.25.\n", - "T (int): Number of iterations. Default is 1.\n", - "seed (int): Random seed value. Default is 42.\n", - "\"\"\"\n", - "\n", - "model = NetMF()\n", - "meta_model = NEU(T=3)\n", - "meta_model.fit(G, model)\n", - "\n", - "embedding = meta_model.get_embedding()\n", - "results = []\n", - "for name,embedding in zip(character_mapping['character'].to_list(), embedding):\n", - " results.append({'character': name, 'embedding': embedding}) \n", - "df = pd.DataFrame.from_dict(results)\n", - "tsne(df)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/tomaz/.local/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:795: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n", + " warnings.warn(\n", + "/home/tomaz/.local/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:805: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "sdzt_FjjeBDB" - }, - "source": [ - "Another node embedding category of algorithms is the structural role embedding category. Instead of capturing the similarity between nodes close in the network (neighbors), we want to capture the similarity between nodes with similar structural roles. One such algorithm is the Role2Vec algorithm.\n", - "\n", - "The default walk_length is 80. Given that our example graph has only 100+ nodes, I have decided to use a smaller walk_length value. Other than that, there is room for more hyper-parameter tweaking." + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from karateclub.node_embedding.meta import NEU\n", + "\n", + "\"\"\"\n", + "L1 (float): Weight of lower order proximities. Defauls is 0.5\n", + "L2 (float): Weight of higer order proximities. Default is 0.25.\n", + "T (int): Number of iterations. Default is 1.\n", + "seed (int): Random seed value. Default is 42.\n", + "\"\"\"\n", + "\n", + "model = NetMF()\n", + "meta_model = NEU(T=3)\n", + "meta_model.fit(G, model)\n", + "\n", + "embedding = meta_model.get_embedding()\n", + "results = []\n", + "for name,embedding in zip(character_mapping['character'].to_list(), embedding):\n", + " results.append({'character': name, 'embedding': embedding}) \n", + "df = pd.DataFrame.from_dict(results)\n", + "tsne(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sdzt_FjjeBDB" + }, + "source": [ + "Another node embedding category of algorithms is the structural role embedding category. Instead of capturing the similarity between nodes close in the network (neighbors), we want to capture the similarity between nodes with similar structural roles. One such algorithm is the Role2Vec algorithm.\n", + "\n", + "The default walk_length is 80. Given that our example graph has only 100+ nodes, I have decided to use a smaller walk_length value. Other than that, there is room for more hyper-parameter tweaking." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 675 }, + "id": "RKFabIOmnL6O", + "outputId": "8f94df1c-7ed4-47c3-aeb3-df8fc3cc0323" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 675 - }, - "id": "RKFabIOmnL6O", - "outputId": "8f94df1c-7ed4-47c3-aeb3-df8fc3cc0323" - }, - "source": [ - "from karateclub.node_embedding.structural import Role2Vec\n", - "\n", - "\"\"\"\n", - "walk_number (int): Number of random walks. Default is 10.\n", - "walk_length (int): Length of random walks. Default is 80.\n", - "dimensions (int): Dimensionality of embedding. Default is 128.\n", - "workers (int): Number of cores. Default is 4.\n", - "window_size (int): Matrix power order. Default is 2.\n", - "epochs (int): Number of epochs. Default is 1.\n", - "learning_rate (float): HogWild! learning rate. Default is 0.05.\n", - "down_sampling (float): Down sampling frequency. Default is 0.0001.\n", - "min_count (int): Minimal count of feature occurrences. Default is 10.\n", - "wl_iterations (int): Number of Weisfeiler-Lehman hashing iterations. Default is 2.\n", - "seed (int): Random seed value. Default is 42.\n", - "erase_base_features (bool): Removing the base features. Default is False.\n", - "\"\"\"\n", - "\n", - "model = Role2Vec(walk_length=20)\n", - "model.fit(G)\n", - "embedding = model.get_embedding()\n", - "\n", - "results = []\n", - "for name,embedding in zip(character_mapping['character'].to_list(), embedding):\n", - " results.append({'character': name, 'embedding': embedding}) \n", - "df = pd.DataFrame.from_dict(results)\n", - "tsne(df)" - ], - "execution_count": 15, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n", - " FutureWarning,\n", - "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n", - " FutureWarning,\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/tomaz/.local/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:795: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n", + " warnings.warn(\n", + "/home/tomaz/.local/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:805: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "H_ij2S-ze_NJ" - }, - "source": [ - "Calculating node embedding based on node role similarity is an exciting field. Instead of comparing the closeness of nodes in the network, we want to capture the structural role similarity between nodes. Then, we can use the structural role embedding to infer a kNN network and run a community detection algorithm to try and segment the nodes based on their network roles. First, we have to store the Role2vec results back to Neo4j." + "data": { + "image/png": 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\n", 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" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from karateclub.node_embedding.structural import Role2Vec\n", + "\n", + "\"\"\"\n", + "walk_number (int): Number of random walks. Default is 10.\n", + "walk_length (int): Length of random walks. Default is 80.\n", + "dimensions (int): Dimensionality of embedding. Default is 128.\n", + "workers (int): Number of cores. Default is 4.\n", + "window_size (int): Matrix power order. Default is 2.\n", + "epochs (int): Number of epochs. Default is 1.\n", + "learning_rate (float): HogWild! learning rate. Default is 0.05.\n", + "down_sampling (float): Down sampling frequency. Default is 0.0001.\n", + "min_count (int): Minimal count of feature occurrences. Default is 10.\n", + "wl_iterations (int): Number of Weisfeiler-Lehman hashing iterations. Default is 2.\n", + "seed (int): Random seed value. Default is 42.\n", + "erase_base_features (bool): Removing the base features. Default is False.\n", + "\"\"\"\n", + "\n", + "model = Role2Vec(walk_length=20)\n", + "model.fit(G)\n", + "embedding = model.get_embedding()\n", + "\n", + "results = []\n", + "for name,embedding in zip(character_mapping['character'].to_list(), embedding):\n", + " results.append({'character': name, 'embedding': embedding}) \n", + "df = pd.DataFrame.from_dict(results)\n", + "tsne(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H_ij2S-ze_NJ" + }, + "source": [ + "Calculating node embedding based on node role similarity is an exciting field. Instead of comparing the closeness of nodes in the network, we want to capture the structural role similarity between nodes. Then, we can use the structural role embedding to infer a kNN network and run a community detection algorithm to try and segment the nodes based on their network roles. First, we have to store the Role2vec results back to Neo4j." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "F6C3WsDuokMr", + "outputId": "279c4b5f-2bb1-47d0-e019-1c26a925561d" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - }, - "id": "F6C3WsDuokMr", - "outputId": "279c4b5f-2bb1-47d0-e019-1c26a925561d" - }, - "source": [ - "df['embedding'] = [el.tolist() for el in df['embedding']]\n", - "data = list(df[['character','embedding']].T.to_dict().values())\n", - "\n", - "read_query(\"\"\"\n", - "UNWIND $data as row\n", - "MATCH (c:Character{name:row.character})\n", - "SET c.role2vec = row.embedding\n", - "RETURN distinct 'done'\n", - "\"\"\", {'data':data})" + "data": { + "text/html": [ + "
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" ], - "execution_count": 17, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " nodeProjection \\\n", - "0 {'Character': {'label': 'Character', 'properti... \n", - "\n", - " relationshipProjection graphName nodeCount \\\n", - "0 {'INTERACTS': {'orientation': 'NATURAL', 'aggr... role2vec 119 \n", - "\n", - " relationshipCount projectMillis \n", - "0 406 23 " - ], - "text/html": [ - "\n", - "
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 17 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TvOobvm5fDzh" - }, - "source": [ - "We don’t care about undirected INTERACTS relationships as we will not be using them. The important thing is that we have included the role2vec node embedding in our projection. Now, we can go ahead and mutate the kNN algorithm. Using the mutate method, we store the algorithm results back to the projected named graph instead of the Neo4j stored graph. This way, we can use the results of the kNN algorithm as an input to a community detection algorithm." + "text/plain": [ + " nodeProjection \\\n", + "0 {'Character': {'label': 'Character', 'properti... \n", + "\n", + " relationshipProjection graphName nodeCount \\\n", + "0 {'INTERACTS': {'orientation': 'NATURAL', 'inde... role2vec 119 \n", + "\n", + " relationshipCount projectMillis \n", + "0 406 96 " ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#KNN\n", + "\n", + "read_query(\"\"\"\n", + "CALL gds.graph.project('role2vec', 'Character', 'INTERACTS', {nodeProperties:['role2vec']})\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TvOobvm5fDzh" + }, + "source": [ + "We don’t care about undirected INTERACTS relationships as we will not be using them. The important thing is that we have included the role2vec node embedding in our projection. Now, we can go ahead and mutate the kNN algorithm. Using the mutate method, we store the algorithm results back to the projected named graph instead of the Neo4j stored graph. This way, we can use the results of the kNN algorithm as an input to a community detection algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 159 }, + "id": "H9OH_uTXqevH", + "outputId": "a3ab7a29-7d59-441a-b44c-89ade2e53ab0" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 159 - }, - "id": "H9OH_uTXqevH", - "outputId": "a3ab7a29-7d59-441a-b44c-89ade2e53ab0" - }, - "source": [ - "read_query(\"\"\"\n", - "CALL gds.knn.mutate('role2vec', {topK: 5, nodeProperties:'role2vec', mutateProperty:'weight', mutateRelationshipType:'SIMILAR_ROLE'})\n", - "\"\"\")" + "data": { + "text/html": [ + "
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ranIterationsnodePairsConsidereddidConvergepreProcessingMilliscomputeMillismutateMillispostProcessingMillisnodesComparedrelationshipsWrittensimilarityDistributionconfiguration
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ranIterationsnodePairsConsidereddidConvergepreProcessingMilliscomputeMillismutateMillispostProcessingMillisnodesComparedrelationshipsWrittensimilarityDistributionconfiguration
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0131190.612429[0.6124285008120895]18{'p99': 33, 'min': 3, 'max': 33, 'mean': 14.87...211202{'maxIterations': 10, 'writeConcurrency': 4, '...
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writeMillisnodePropertiesWrittenmodularitymodularitiesranLevelscommunityCountcommunityDistributionpostProcessingMillispreProcessingMilliscomputeMillisconfiguration
01921190.712916[0.712915754537109]110{'p99': 29, 'min': 3, 'max': 29, 'mean': 11.9,...40701{'maxIterations': 10, 'writeConcurrency': 4, '...
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" ], - "execution_count": 20, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " writeMillis graphName relationshipType relationshipProperty \\\n", - "0 21 role2vec SIMILAR_ROLE weight \n", - "\n", - " relationshipsWritten propertiesWritten \n", - "0 595 595 " - ], - "text/html": [ - "\n", - "
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Hopefully, this simple integration of the Neo4j and Karate Club project will help you use the node embedding models that will work best for you." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "qIE2xSAgfQai" - }, - "source": [ - "" - ], - "execution_count": null, - "outputs": [] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" } - ] -} \ No newline at end of file + ], + "source": [ + "read_query(\"\"\"\n", + "CALL gds.graph.writeRelationship('role2vec', 'SIMILAR_ROLE', 'weight')\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gwtNKPoDfPOI" + }, + "source": [ + "# Conclusion\n", + "The Karate Club package includes node embedding models that take into consideration also node properties. Unfortunately, we don’t have any node properties in our simple Harry Potter network, so I skipped them. Nevertheless, the node embedding research field is fascinating, and there are many approaches to what type of information you want to extract from the network. Hopefully, this simple integration of the Neo4j and Karate Club project will help you use the node embedding models that will work best for you." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qIE2xSAgfQai" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyOelByQliYNdEZtLAG5ICeE", + "include_colab_link": true, + "name": "Harry Potter - Karate Club integration.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/ice&fire/Ice&Fire_analysis.ipynb b/ice&fire/Ice&Fire_analysis.ipynb index 1944a79..39dc641 100644 --- a/ice&fire/Ice&Fire_analysis.ipynb +++ b/ice&fire/Ice&Fire_analysis.ipynb @@ -1,930 +1,625 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "base_uri": "https://localhost:8080/" }, - "language_info": { - "name": "python" + "id": "pK9aJPSTWznM", + "outputId": "cf054629-bb16-4b56-95b0-2ea596de7eab" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting graphdatascience\n", + " Downloading graphdatascience-1.5-py3-none-any.whl (183 kB)\n", + "\u001b[K |████████████████████████████████| 183 kB 5.1 MB/s \n", + "\u001b[?25hCollecting multimethod<2.0,>=1.0\n", + " Downloading multimethod-1.9-py3-none-any.whl (10 kB)\n", + "Requirement already satisfied: pandas<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (1.3.5)\n", + "Requirement already satisfied: tqdm<5.0,>=4.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (4.64.1)\n", + "Collecting neo4j<6.0,>=4.4.2\n", + " Downloading neo4j-5.2.0.tar.gz (173 kB)\n", + "\u001b[K |████████████████████████████████| 173 kB 38.6 MB/s \n", + "\u001b[?25hRequirement already satisfied: pyarrow<11.0,>=4.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (6.0.1)\n", + "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j<6.0,>=4.4.2->graphdatascience) (2022.6)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (2.8.2)\n", + "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (1.21.6)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas<2.0,>=1.0->graphdatascience) (1.15.0)\n", + "Building wheels for collected packages: neo4j\n", + " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for neo4j: filename=neo4j-5.2.0-py3-none-any.whl size=248021 sha256=7eca51544ff2688bb0223e2c4bad8b9d4f6ad129b0fa70670353498914a04c33\n", + " Stored in directory: /root/.cache/pip/wheels/5a/07/16/4d845d69ef310660c14b7148848c95da3ef3950c7b58daec42\n", + "Successfully built neo4j\n", + "Installing collected packages: neo4j, multimethod, graphdatascience\n", + "Successfully installed graphdatascience-1.5 multimethod-1.9 neo4j-5.2.0\n" + ] } + ], + "source": [ + "!pip install graphdatascience" + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pK9aJPSTWznM", - "outputId": "cf054629-bb16-4b56-95b0-2ea596de7eab" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Collecting graphdatascience\n", - " Downloading graphdatascience-1.5-py3-none-any.whl (183 kB)\n", - "\u001b[K |████████████████████████████████| 183 kB 5.1 MB/s \n", - "\u001b[?25hCollecting multimethod<2.0,>=1.0\n", - " Downloading multimethod-1.9-py3-none-any.whl (10 kB)\n", - "Requirement already satisfied: pandas<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (1.3.5)\n", - "Requirement already satisfied: tqdm<5.0,>=4.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (4.64.1)\n", - "Collecting neo4j<6.0,>=4.4.2\n", - " Downloading neo4j-5.2.0.tar.gz (173 kB)\n", - "\u001b[K |████████████████████████████████| 173 kB 38.6 MB/s \n", - "\u001b[?25hRequirement already satisfied: pyarrow<11.0,>=4.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (6.0.1)\n", - "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j<6.0,>=4.4.2->graphdatascience) (2022.6)\n", - "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (2.8.2)\n", - "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (1.21.6)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas<2.0,>=1.0->graphdatascience) (1.15.0)\n", - "Building wheels for collected packages: neo4j\n", - " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for neo4j: filename=neo4j-5.2.0-py3-none-any.whl size=248021 sha256=7eca51544ff2688bb0223e2c4bad8b9d4f6ad129b0fa70670353498914a04c33\n", - " Stored in directory: /root/.cache/pip/wheels/5a/07/16/4d845d69ef310660c14b7148848c95da3ef3950c7b58daec42\n", - "Successfully built neo4j\n", - "Installing collected packages: neo4j, multimethod, graphdatascience\n", - "Successfully installed graphdatascience-1.5 multimethod-1.9 neo4j-5.2.0\n" - ] - } - ], - "source": [ - "!pip install graphdatascience" - ] - }, - { - "cell_type": "code", - "source": [ - "from graphdatascience import GraphDataScience\n", - "\n", - "host = \"bolt://44.202.221.209:7687\"\n", - "user = \"neo4j\"\n", - "password = \"map-striker-injuries\"\n", - "\n", - "gds = GraphDataScience(host, auth=(user, password))" - ], - "metadata": { - "id": "KdsdF8zSW1yN" - }, - "execution_count": 2, - "outputs": [] + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "KdsdF8zSW1yN" + }, + "outputs": [], + "source": [ + "from graphdatascience import GraphDataScience\n", + "\n", + "host = \"bolt://3.231.25.240:7687\"\n", + "user = \"neo4j\"\n", + "password = \"hatchets-visitor-axes\"\n", + "\n", + "gds = GraphDataScience(host, auth=(user, password))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "5Un_5pO3XE_S", + "outputId": "15388f90-713a-409c-a03d-5c819caa2fc0" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "# Results of the query changed for notebook environment\n", - "gds.run_cypher(\n", - " \"\"\"MATCH (s:Character {name:$person1}), (t:Character {name:$person2})\n", - "MATCH p=shortestPath((s)-[:FATHER|MOTHER|SPOUSE*]-(t))\n", - "RETURN [n in nodes(p) | n.name] AS result\"\"\",\n", - " {\"person1\": \"Tyrion Lannister\", \"person2\": \"Viserys I\"},\n", - ")[\"result\"][0]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5Un_5pO3XE_S", - "outputId": "15388f90-713a-409c-a03d-5c819caa2fc0" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "['Tyrion Lannister',\n", - " 'LadyJoanna Lannister',\n", - " 'Cersei Lannister',\n", - " 'Robert I',\n", - " 'Steffon Baratheon',\n", - " 'Rhaelle Targaryen',\n", - " 'Aegon V',\n", - " 'Maekar I',\n", - " 'Daeron II',\n", - " 'Aegon IV',\n", - " 'Viserys II',\n", - " 'Rhaenyra Targaryen',\n", - " 'Viserys I']" - ] - }, - "metadata": {}, - "execution_count": 3 - } + "data": { + "text/plain": [ + "['Tyrion Lannister',\n", + " 'Sansa Stark',\n", + " 'Eddard Stark',\n", + " 'Lyarra Stark',\n", + " 'Rodrik Stark',\n", + " 'Beron Stark',\n", + " 'Brandon Stark',\n", + " 'Cregan Stark',\n", + " 'Rickon Stark',\n", + " 'Sara Snow',\n", + " 'Jacaerys Velaryon',\n", + " 'Rhaenyra Targaryen',\n", + " 'Viserys I']" ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Results of the query changed for notebook environment\n", + "gds.run_cypher(\n", + " \"\"\"MATCH (s:Character {name:$person1}), (t:Character {name:$person2})\n", + "MATCH p=shortestPath((s)-[:FATHER|MOTHER|SPOUSE*]-(t))\n", + "RETURN [n in nodes(p) | n.name] AS result\"\"\",\n", + " {\"person1\": \"Tyrion Lannister\", \"person2\": \"Viserys I\"},\n", + ")[\"result\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "2YqgteubXto8", + "outputId": "c55f2dfa-95e9-4b03-ea9c-8ca17c6f8d78" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "# Results of the query changed for notebook environment\n", - "gds.run_cypher(\n", - " \"\"\"MATCH p=(c:Character {name:$person})-[:FATHER|MOTHER*]->()\n", - "RETURN [r in relationships(p) | endNode(r).name + \" \" + type(r) + \" to \" + startNode(r).name] AS result\"\"\",\n", - " {\"person\": \"Margaery Tyrell\"},\n", - ")[\"result\"]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "2YqgteubXto8", - "outputId": "c55f2dfa-95e9-4b03-ea9c-8ca17c6f8d78" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0 [Mace Tyrell FATHER to Margaery Tyrell]\n", - "1 [Mace Tyrell FATHER to Margaery Tyrell, Olenna...\n", - "2 [Mace Tyrell FATHER to Margaery Tyrell, Olenna...\n", - "3 [Mace Tyrell FATHER to Margaery Tyrell, Luthor...\n", - "4 [Alerie Hightower MOTHER to Margaery Tyrell]\n", - "5 [Alerie Hightower MOTHER to Margaery Tyrell, L...\n", - "Name: result, dtype: object" - ] - }, - "metadata": {}, - "execution_count": 4 - } + "data": { + "text/plain": [ + "0 [Alerie Hightower MOTHER to Margaery Tyrell]\n", + "1 [Alerie Hightower MOTHER to Margaery Tyrell, L...\n", + "2 [Mace Tyrell FATHER to Margaery Tyrell]\n", + "3 [Mace Tyrell FATHER to Margaery Tyrell, Olenna...\n", + "4 [Mace Tyrell FATHER to Margaery Tyrell, Olenna...\n", + "5 [Mace Tyrell FATHER to Margaery Tyrell, Luthor...\n", + "Name: result, dtype: object" ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Results of the query changed for notebook environment\n", + "gds.run_cypher(\n", + " \"\"\"MATCH p=(c:Character {name:$person})-[:FATHER|MOTHER*]->()\n", + "RETURN [r in relationships(p) | endNode(r).name + \" \" + type(r) + \" to \" + startNode(r).name] AS result\"\"\",\n", + " {\"person\": \"Margaery Tyrell\"},\n", + ")[\"result\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "t9JdS7ibW4Q0" + }, + "outputs": [], + "source": [ + "G, res = gds.graph.project(\"family\", \"Character\", [\"MOTHER\", \"FATHER\", \"SPOUSE\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "MzJFNjK-W_tm" + }, + "outputs": [], + "source": [ + "wcc_df = gds.wcc.stream(G)\n", + "wcc_df[\"name\"] = [el[\"name\"] for el in gds.util.asNodes(wcc_df[\"nodeId\"].to_list())]\n", + "wcc_df[\"last_name\"] = [\n", + " el.split(\" \")[-1] if len(el.split(\" \")) > 1 and len(el.split(\" \")[-1]) > 3 else None\n", + " for el in wcc_df[\"name\"]\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 }, + "id": "7IciN9YNZMmQ", + "outputId": "6b0bb5a4-c962-417c-c7d1-4fc050f92873" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "G, res = gds.graph.project(\"family\", \"Character\", [\"MOTHER\", \"FATHER\", \"SPOUSE\"])" - ], - "metadata": { - "id": "t9JdS7ibW4Q0" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "wcc_df = gds.wcc.stream(G)\n", - "wcc_df[\"name\"] = [el[\"name\"] for el in gds.util.asNodes(wcc_df[\"nodeId\"].to_list())]\n", - "wcc_df[\"last_name\"] = [\n", - " el.split(\" \")[-1] if len(el.split(\" \")) > 1 and len(el.split(\" \")[-1]) > 3 else None\n", - " for el in wcc_df[\"name\"]\n", - "]" - ], - "metadata": { - "id": "MzJFNjK-W_tm" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "wcc_df.head()" + "data": { + "text/html": [ + "
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spouse1spouse2
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" ], - "metadata": { - "id": "Mll03EJPj6ew", - "outputId": "cb78ae3a-ea0c-42da-941a-3a9bc4ef90c5", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " spouse1 spouse2\n", - "0 Jacaerys Velaryon Sara Snow" - ], - "text/html": [ - "\n", - "
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0Jacaerys VelaryonSara Snow
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{ - "output_type": "execute_result", - "data": { - "text/plain": [ - "graphName family\n", - "database neo4j\n", - "memoryUsage \n", - "sizeInBytes -1\n", - "nodeCount 3654\n", - "relationshipCount 1794\n", - "configuration {'relationshipProjection': {'FATHER': {'orient...\n", - "density 0.000134\n", - "creationTime 2022-11-08T17:57:36.072746000+00:00\n", - "modificationTime 2022-11-08T17:57:36.181971000+00:00\n", - "schema {'graphProperties': {}, 'relationships': {'FAT...\n", - "Name: 0, dtype: object" - ] - }, - "metadata": {}, - "execution_count": 35 - } + "data": { + "text/plain": [ + "graphName family\n", + "database neo4j\n", + "memoryUsage \n", + "sizeInBytes -1\n", + "nodeCount 3653\n", + "relationshipCount 1794\n", + "configuration {'relationshipProjection': {'FATHER': {'orient...\n", + "density 0.000134\n", + "creationTime 2023-02-01T12:28:33.121376800+00:00\n", + "modificationTime 2023-02-01T12:28:33.233263361+00:00\n", + "schema {'graphProperties': {}, 'relationships': {'FAT...\n", + "schemaWithOrientation {'graphProperties': {}, 'relationships': {'FAT...\n", + "Name: 0, dtype: object" ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "XmmwDND9a7qL" - }, - "execution_count": null, - "outputs": [] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } - ] -} \ No newline at end of file + ], + "source": [ + "G.drop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XmmwDND9a7qL" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/ice&fire/Ice&Fire_import.ipynb b/ice&fire/Ice&Fire_import.ipynb index eeaa56d..d42293a 100644 --- a/ice&fire/Ice&Fire_import.ipynb +++ b/ice&fire/Ice&Fire_import.ipynb @@ -1,1763 +1,983 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "base_uri": "https://localhost:8080/" }, - "language_info": { - "name": "python" + "id": "R_hqa9eZDO2M", + "outputId": "2a1256f4-e99b-40bd-c770-1c8a8806bc1b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting graphdatascience\n", + " Downloading graphdatascience-1.5-py3-none-any.whl (183 kB)\n", + "\u001b[K |████████████████████████████████| 183 kB 13.2 MB/s \n", + "\u001b[?25hCollecting multimethod<2.0,>=1.0\n", + " Downloading multimethod-1.9-py3-none-any.whl (10 kB)\n", + "Requirement already satisfied: tqdm<5.0,>=4.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (4.64.1)\n", + "Requirement already satisfied: pyarrow<11.0,>=4.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (6.0.1)\n", + "Collecting neo4j<6.0,>=4.4.2\n", + " Downloading neo4j-5.2.0.tar.gz (173 kB)\n", + "\u001b[K |████████████████████████████████| 173 kB 62.5 MB/s \n", + "\u001b[?25hRequirement already satisfied: pandas<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (1.3.5)\n", + "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j<6.0,>=4.4.2->graphdatascience) (2022.6)\n", + "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (1.21.6)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas<2.0,>=1.0->graphdatascience) (1.15.0)\n", + "Building wheels for collected packages: neo4j\n", + " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for neo4j: filename=neo4j-5.2.0-py3-none-any.whl size=248021 sha256=b1213438cc3b276397b7ec7db728e7913e3d1e99d4b729b71520a854edd87042\n", + " Stored in directory: /root/.cache/pip/wheels/5a/07/16/4d845d69ef310660c14b7148848c95da3ef3950c7b58daec42\n", + "Successfully built neo4j\n", + "Installing collected packages: neo4j, multimethod, graphdatascience\n", + "Successfully installed graphdatascience-1.5 multimethod-1.9 neo4j-5.2.0\n" + ] } + ], + "source": [ + "!pip install graphdatascience" + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "R_hqa9eZDO2M", - "outputId": "2a1256f4-e99b-40bd-c770-1c8a8806bc1b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Collecting graphdatascience\n", - " Downloading graphdatascience-1.5-py3-none-any.whl (183 kB)\n", - "\u001b[K |████████████████████████████████| 183 kB 13.2 MB/s \n", - "\u001b[?25hCollecting multimethod<2.0,>=1.0\n", - " Downloading multimethod-1.9-py3-none-any.whl (10 kB)\n", - "Requirement already satisfied: tqdm<5.0,>=4.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (4.64.1)\n", - "Requirement already satisfied: pyarrow<11.0,>=4.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (6.0.1)\n", - "Collecting neo4j<6.0,>=4.4.2\n", - " Downloading neo4j-5.2.0.tar.gz (173 kB)\n", - "\u001b[K |████████████████████████████████| 173 kB 62.5 MB/s \n", - "\u001b[?25hRequirement already satisfied: pandas<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from graphdatascience) (1.3.5)\n", - "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from neo4j<6.0,>=4.4.2->graphdatascience) (2022.6)\n", - "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (1.21.6)\n", - "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0,>=1.0->graphdatascience) (2.8.2)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas<2.0,>=1.0->graphdatascience) (1.15.0)\n", - "Building wheels for collected packages: neo4j\n", - " Building wheel for neo4j (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for neo4j: filename=neo4j-5.2.0-py3-none-any.whl size=248021 sha256=b1213438cc3b276397b7ec7db728e7913e3d1e99d4b729b71520a854edd87042\n", - " Stored in directory: /root/.cache/pip/wheels/5a/07/16/4d845d69ef310660c14b7148848c95da3ef3950c7b58daec42\n", - "Successfully built neo4j\n", - "Installing collected packages: neo4j, multimethod, graphdatascience\n", - "Successfully installed graphdatascience-1.5 multimethod-1.9 neo4j-5.2.0\n" - ] - } - ], - "source": [ - "!pip install graphdatascience" - ] - }, - { - "cell_type": "code", - "source": [ - "from graphdatascience import GraphDataScience\n", - "\n", - "host = \"bolt://44.202.221.209:7687\"\n", - "user = \"neo4j\"\n", - "password = \"map-striker-injuries\"\n", - "\n", - "gds = GraphDataScience(host, auth=(user, password))" - ], - "metadata": { - "id": "Eu_aAgmYiaGj" - }, - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# Define constraints" - ], - "metadata": { - "id": "lZsg0OoBM_8L" - } + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "Eu_aAgmYiaGj" + }, + "outputs": [], + "source": [ + "from graphdatascience import GraphDataScience\n", + "\n", + "host = \"bolt://3.231.25.240:7687\"\n", + "user = \"neo4j\"\n", + "password = \"hatchets-visitor-axes\"\n", + "\n", + "gds = GraphDataScience(host, auth=(user, password))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lZsg0OoBM_8L" + }, + "source": [ + "# Define constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49 }, + "id": "rN310oX6lH_4", + "outputId": "8e51e2be-d2eb-4948-cb4c-569163abc533" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "gds.run_cypher(\n", - " \"\"\"\n", - "CREATE CONSTRAINT IF NOT EXISTS ON (h:Faction) ASSERT (h.url) IS UNIQUE; \n", - "\"\"\"\n", - ")" + "data": { + "text/html": [ + "
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 5 - } + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gds.run_cypher(\n", + " \"\"\"\n", + "call dbms.setConfigValue('dbms.transaction.timeout','0')\n", + "\"\"\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49 }, + "id": "RYXOh0A4lU9R", + "outputId": "65c98346-9253-443f-82df-70f933039182" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "gds.run_cypher(\n", - " \"\"\"\n", - "LOAD CSV WITH HEADERS FROM \"https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/ice_fire/characters.tsv\" AS row FIELDTERMINATOR \"\\t\"\n", - "MERGE (c:Character {url: toLower(row.url)})\n", - "SET c.name = row.name,\n", - " c.born = replace(replace(replace(row.born, \"[\", \"\"), \"]\", \"\"),\"b'\",\"\"),\n", - " c.died = replace(replace(replace(row.died, \"[\", \"\"), \"]\", \"\"),\"b'\",\"\"),\n", - " c.title = replace(replace(replace(row.title, \"[\", \"\"), \"]\", \"\"),\"b'\",\"\")\n", - "FOREACH (a IN apoc.convert.fromJsonList(row.allegiance) | MERGE (f:Faction {url: toLower(split(a, \"//\")[1])}) MERGE (c)-[:ALLEGIANCE]->(f))\n", - "FOREACH (cu IN apoc.convert.fromJsonList(row.culture) | MERGE (culture:Culture {name: split(toLower(cu), \"[\")[0]}) MERGE (c)-[:CULTURE]->(culture))\n", - "FOREACH (s IN apoc.convert.fromJsonList(row.spouse) | MERGE (c1:Character {url: toLower(s)}) MERGE (c)-[:SPOUSE]-(c1))\n", - "FOREACH (s IN apoc.convert.fromJsonList(row.father) | MERGE (c1:Character {url: toLower(s)}) MERGE (c)-[:FATHER]->(c1))\n", - "FOREACH (m IN apoc.convert.fromJsonList(row.mother) | MERGE (c1:Character {url: toLower(m)}) MERGE (c)-[:MOTHER]->(c1))\n", - "FOREACH (b IN apoc.convert.fromJsonList(row.books) | MERGE (b1:Book {url: toLower(split(b, \"//\")[1])}) MERGE (c)-[:APPEARED_IN_BOOK]->(b1))\n", - "FOREACH (s IN apoc.convert.fromJsonList(row.show) | MERGE (s1:Show {url: toLower(split(s, \"//\")[1])}) MERGE (c)-[:APPEARED_IN_BOOK]->(s1))\n", - "FOREACH (pr IN apoc.convert.fromJsonList(row.predecessor) | MERGE (c1:Character {url: toLower(pr)}) MERGE (c)-[:PREDECESSOR]->(c1))\n", - "FOREACH (pr IN apoc.convert.fromJsonList(row.successor) | MERGE (c1:Character {url: toLower(pr)}) MERGE (c)<-[:PREDECESSOR]-(c1))\n", - "\"\"\"\n", - ")" + "data": { + "text/html": [ + "
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" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49 - }, - "id": "RYXOh0A4lU9R", - "outputId": "65c98346-9253-443f-82df-70f933039182" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: []" - ], - "text/html": [ - "\n", - "
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\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49 - }, - "id": "TSttWqMYFlP0", - "outputId": "8f2b5cfe-eb3c-4dc0-e04c-cc0385b14944" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: []" - ], - "text/html": [ - "\n", - "
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\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 7 - } + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gds.run_cypher(\n", + " \"\"\"\n", + "MATCH (n)\n", + "WHERE NOT n.name IS NOT NULL\n", + "WITH n, replace(split(apoc.text.urldecode(n.url), \"/\")[-1], \"_\", \" \") AS clean_name\n", + "SET n.name = clean_name\n", + "\"\"\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "cCONUTgssmqY", + "outputId": "7a08235b-0d32-4b0a-d95a-3d7aa7d64d6f" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "gds.run_cypher(\n", - " \"\"\"\n", - "MATCH (t)-[r]->(m)\n", - "WHERE toLower(t.name) = toLower(m.name)\n", - "DELETE r\n", - "RETURN count(*) AS selfloops\n", - "\"\"\"\n", - ")" + "data": { + "text/html": [ + "
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selfloops
0329
\n", + "
" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - }, - "id": "cCONUTgssmqY", - "outputId": "7a08235b-0d32-4b0a-d95a-3d7aa7d64d6f" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " selfloops\n", - "0 329" - ], - "text/html": [ - "\n", - "
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selfloops
0329
\n", - "
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\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 8 - } + "text/plain": [ + " selfloops\n", + "0 329" ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gds.run_cypher(\n", + " \"\"\"\n", + "MATCH (t)-[r]->(m)\n", + "WHERE toLower(t.name) = toLower(m.name)\n", + "DELETE r\n", + "RETURN count(*) AS selfloops\n", + "\"\"\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "f0VCfRMNJYw-", + "outputId": "ae6513b1-8357-41fe-ac30-738dee59e9ed" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "gds.run_cypher(\n", - " \"\"\"\n", - "MATCH (n) \n", - "WHERE NOT (n)--()\n", - "DELETE n\n", - "RETURN count(*) AS isolated\n", - "\"\"\"\n", - ")" + "data": { + "text/html": [ + "
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isolated
0221
\n", + "
" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - }, - "id": "f0VCfRMNJYw-", - "outputId": "ae6513b1-8357-41fe-ac30-738dee59e9ed" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " isolated\n", - "0 221" - ], - "text/html": [ - "\n", - "
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isolated
0221
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 9 - } + "text/plain": [ + " isolated\n", + "0 221" ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gds.run_cypher(\n", + " \"\"\"\n", + "MATCH (n) \n", + "WHERE NOT EXISTS { (n)--() }\n", + "DELETE n\n", + "RETURN count(*) AS isolated\n", + "\"\"\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "o59beDs6MXhg", + "outputId": "c6b050d6-319c-4abc-e896-48c60104da69" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "gds.run_cypher(\n", - " \"\"\"\n", - "MATCH (s:Show)\n", - "WHERE NOT s.url CONTAINS \"house\"\n", - "WITH s, split(s.url, \"_\")[-1] AS seasons\n", - "WITH seasons, collect(s) AS duplicates\n", - "WHERE size(duplicates) > 1\n", - "CALL apoc.refactor.mergeNodes(duplicates) YIELD node\n", - "RETURN distinct 'done' AS result\n", - "\"\"\"\n", - ")" + "data": { + "text/html": [ + "
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result
0done
\n", + "
" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - }, - "id": "o59beDs6MXhg", - "outputId": "c6b050d6-319c-4abc-e896-48c60104da69" - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " result\n", - "0 done" - ], - "text/html": [ - "\n", - "
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result
0done
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 10 - } + "text/plain": [ + " result\n", + "0 done" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gds.run_cypher(\n", + " \"\"\"\n", + "MATCH (s:Show)\n", + "WHERE NOT s.url CONTAINS \"house\"\n", + "WITH s, split(s.url, \"_\")[-1] AS seasons\n", + "WITH seasons, collect(s) AS duplicates\n", + "WHERE size(duplicates) > 1\n", + "CALL apoc.refactor.mergeNodes(duplicates) YIELD node\n", + "RETURN distinct 'done' AS result\n", + "\"\"\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 }, + "id": "-8o0rpISh_YN", + "outputId": "76737527-a69a-4b19-b656-ab879c604bf1" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "gds.run_cypher(\"\"\"\n", - "MATCH (c1:Character), (c2:Character)\n", - "WHERE c1.name CONTAINS \"catelyn tully\" AND c2.name CONTAINS \"Catelyn Stark\"\n", - "CALL apoc.refactor.mergeNodes([c2,c1]) YIELD node\n", - "RETURN distinct 'done'\n", - "\"\"\")" + "data": { + "text/html": [ + "
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'done'
0done
\n", + "
" ], - "metadata": { - "id": "-8o0rpISh_YN", - "outputId": "76737527-a69a-4b19-b656-ab879c604bf1", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " 'done'\n", - "0 done" - ], - "text/html": [ - "\n", - "
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'done'
0done
\n", - "
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\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 11 - } + "text/plain": [ + " 'done'\n", + "0 done" ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gds.run_cypher(\"\"\"\n", + "MATCH (c1:Character), (c2:Character)\n", + "WHERE c1.name CONTAINS \"catelyn tully\" AND c2.name CONTAINS \"Catelyn Stark\"\n", + "CALL apoc.refactor.mergeNodes([c2,c1]) YIELD node\n", + "RETURN distinct 'done'\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5P1_Yq17NF9s" + }, + "source": [ + "# Verify the data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 }, + "id": "VBziu9xioGFJ", + "outputId": "1d1200a7-39d1-47b5-eb25-87d93d3af5c6" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "# Verify the data" - ], - "metadata": { - "id": "5P1_Yq17NF9s" - } - }, - { - "cell_type": "code", - "source": [ - "gds.run_cypher(\n", - " \"\"\"\n", - "CALL apoc.meta.stats()\n", - "\"\"\"\n", - ")" + "data": { + "text/html": [ + "
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labelCountrelTypeCountpropertyKeyCountnodeCountrelCountlabelsrelTypesrelTypesCountstats
07786431916941{'Character': 3653, 'Book': 20, 'Show': 11, 'C...{'(:Character)-[:PREDECESSOR]->()': 307, '()-[...{'APPEARED_IN_BOOK': 7936, 'FATHER': 960, 'MOT...{'relTypeCount': 7, 'propertyKeyCount': 86, 'l...
\n", + "
" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 142 - }, - "id": "VBziu9xioGFJ", - "outputId": "1d1200a7-39d1-47b5-eb25-87d93d3af5c6" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " labelCount relTypeCount propertyKeyCount nodeCount relCount \\\n", - "0 6 7 21 4319 16941 \n", - "\n", - " labels \\\n", - "0 {'Character': 3653, 'Book': 20, 'Show': 11, 'C... \n", - "\n", - " relTypes \\\n", - "0 {'(:Character)-[:PREDECESSOR]->()': 307, '()-[... \n", - "\n", - " relTypesCount \\\n", - "0 {'APPEARED_IN_BOOK': 7936, 'FATHER': 960, 'MOT... \n", - "\n", - " stats \n", - "0 {'relTypeCount': 7, 'propertyKeyCount': 21, 'l... " - ], - "text/html": [ - "\n", - "
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labelCountrelTypeCountpropertyKeyCountnodeCountrelCountlabelsrelTypesrelTypesCountstats
06721431916941{'Character': 3653, 'Book': 20, 'Show': 11, 'C...{'(:Character)-[:PREDECESSOR]->()': 307, '()-[...{'APPEARED_IN_BOOK': 7936, 'FATHER': 960, 'MOT...{'relTypeCount': 7, 'propertyKeyCount': 21, 'l...
\n", - "
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\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 12 - } + "text/plain": [ + " labelCount relTypeCount propertyKeyCount nodeCount relCount \\\n", + "0 7 7 86 4319 16941 \n", + "\n", + " labels \\\n", + "0 {'Character': 3653, 'Book': 20, 'Show': 11, 'C... \n", + "\n", + " relTypes \\\n", + "0 {'(:Character)-[:PREDECESSOR]->()': 307, '()-[... \n", + "\n", + " relTypesCount \\\n", + "0 {'APPEARED_IN_BOOK': 7936, 'FATHER': 960, 'MOT... \n", + "\n", + " stats \n", + "0 {'relTypeCount': 7, 'propertyKeyCount': 86, 'l... " ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gds.run_cypher(\n", + " \"\"\"\n", + "CALL apoc.meta.stats()\n", + "\"\"\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 }, + "id": "YFaql70MHzTh", + "outputId": "528e46d4-cfc3-4ef0-bf0c-0c33a397640d" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "gds.run_cypher(\n", - " \"\"\"\n", - "MATCH (n)\n", - "RETURN labels(n)[0] AS label, count(*) AS count\n", - "\"\"\"\n", - ")" + "data": { + "text/html": [ + "
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labelcount
0Character3653
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