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Going to 2.3.0
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@tomasonjo
tomasonjo committed Feb 1, 2023
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5,290 changes: 2,098 additions & 3,192 deletions Countries_of_the_world/Countries of the world analysis.ipynb
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6,523 changes: 3,265 additions & 3,258 deletions GDS_Multigraph/GDS multigraph.ipynb
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1,298 changes: 622 additions & 676 deletions Game_of_thrones_community_iteration/Game of thrones community iteration.ipynb
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6,923 changes: 2,612 additions & 4,311 deletions Lord_of_the_wikidata/Part1 Importing Wikidata into Neo4j and analyzing family trees.ipynb
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3,714 changes: 0 additions & 3,714 deletions debug/node2vec.ipynb
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120 changes: 68 additions & 52 deletions fraud/p2p-fraud.ipynb
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Expand Up @@ -18,7 +18,7 @@
"### Prepare the Neo4j environment\n",
"\n",
"We will be using Neo4j as the source of truth to train the ML model. Therefore, I suggest you download and install the [Neo4j Desktop application](https://neo4j.com/download/) if you want to follow along with the code examples.\n",
"The dataset is available as a database dump. It is a variation of the database dump available on Neo4j's [product example GitHub to showcase fraud detection](https://github.com/neo4j-product-examples/demo-fraud-detection-with-p2p). I have added the fraud risk labels as described in the [second part of the Exploring fraud detection series](https://neo4j.com/developer-blog/exploring-fraud-detection-neo4j-graph-data-science-part-2/), so you don't have to deal with it. You can download the updated database dump by clicking on [this link](https://drive.google.com/file/d/1_N_QLtCRI-eeLzjEIFZAbj8YQrWfTolI/view?usp=sharing).\n",
"The dataset is available as a database dump. It is a variation of the database dump available on Neo4j's [product example GitHub to showcase fraud detection](https://github.com/neo4j-product-examples/demo-fraud-detection-with-p2p). I have added the fraud risk labels as described in the [second part of the Exploring fraud detection series](https://neo4j.com/developer-blog/exploring-fraud-detection-neo4j-graph-data-science-part-2/), so you don't have to deal with it. You can download the updated database dump by clicking on [this link](https://drive.google.com/file/d/1apR3xwWEOdi_WKmIAGk1bPqhHQSgxwT-/view?usp=sharing).\n",
"I've written a [post about restoring a database dump in Neo4j Desktop](https://tbgraph.wordpress.com/2020/11/11/dump-and-load-a-database-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 2.0.0 of the GDS or later.\n",
"\n",
"### Neo4j Graph Data Science Python client\n",
Expand All @@ -40,7 +40,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"2.1.4\n"
"2.3.0\n"
]
}
],
Expand All @@ -49,7 +49,7 @@
"\n",
"host = \"bolt://localhost:7687\"\n",
"user = \"neo4j\"\n",
"password= \"letmein\"\n",
"password= \"pleaseletmein\"\n",
"\n",
"gds = GraphDataScience(host, auth=(user, password))\n",
"\n",
Expand Down Expand Up @@ -121,7 +121,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"id": "5381aec2",
"metadata": {},
"outputs": [],
Expand All @@ -143,9 +143,9 @@
" \n",
"RETURN u.guid AS user_id,\n",
" u.fraudRisk AS fraudRisk,\n",
" size((u)-[:USED]->()) 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",
Expand All @@ -159,7 +159,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"id": "3ca0b417",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -314,7 +314,7 @@
"4 200.00 2 "
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"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -333,7 +333,7 @@
},
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"id": "66632cba",
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"outputs": [
Expand Down Expand Up @@ -383,7 +383,7 @@
"1 1 211"
]
},
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"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -402,7 +402,7 @@
},
{
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"execution_count": 6,
"execution_count": 7,
"id": "74048cdd",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -608,7 +608,7 @@
"max 6750.000000 564.000000 "
]
},
"execution_count": 6,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -629,7 +629,7 @@
},
{
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"id": "cb367423",
"metadata": {},
"outputs": [
Expand All @@ -639,7 +639,7 @@
"<AxesSubplot:>"
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"id": "d3abee73",
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Expand All @@ -698,7 +698,7 @@
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{
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Expand Down Expand Up @@ -746,7 +746,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 12,
"id": "19e86468",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -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": {
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"version_major": 2,
"version_minor": 0
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"text/plain": [
"Loading: 0%| | 0/100 [00:00<?, ?%/s]"
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"metadata": {},
"output_type": "display_data"
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],
"source": [
"G, res = gds.graph.project('fraud', ['User', 'Card'], \n",
" {'HAS_CC':{\n",
Expand Down Expand Up @@ -872,7 +887,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 14,
"id": "d55901cc",
"metadata": {},
"outputs": [],
Expand All @@ -892,7 +907,7 @@
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{
"cell_type": "code",
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Expand Down Expand Up @@ -924,27 +939,27 @@
" <tbody>\n",
" <tr>\n",
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Expand All @@ -953,14 +968,14 @@
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"1 1 1\n",
"2 2 2\n",
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"0 765184 0\n",
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Expand All @@ -979,7 +994,7 @@
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Expand All @@ -1000,7 +1015,7 @@
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Expand All @@ -1025,7 +1040,7 @@
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Expand All @@ -1051,7 +1066,7 @@
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Expand All @@ -1073,7 +1088,7 @@
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Expand All @@ -1096,7 +1111,7 @@
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Expand Down Expand Up @@ -1167,28 +1182,29 @@
},
{
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"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"
}
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