Page Rank Project

This project is an implementation of a PageRank algorithm using C, designed to analyze directed graphs and compute the PageRank of its vertices. This project is part of the Saarland University Programming 2 course for the Summer Term 2024.

Table of Contents

• Overview
• Installation
• Usage
• Features
• Contributing

Overview

The goal of this project is to compute the PageRank of vertices in a directed graph, which can be considered as websites connected by hyperlinks. The implementation uses two methods to calculate the PageRank:

1. Random Surfer Model: Simulates the behavior of a random surfer navigating through the network of websites to approximate PageRank.
2. Markov Chain Model: Uses mathematical foundations to compute PageRank with higher precision.

Installation

To compile and run the program, ensure you have a C compiler installed (e.g., gcc). Follow the steps below:

1. Clone the repository:

   git clone https://github.com/yourusername/pagerank.git
   cd pagerank

2. Compile the project using make

   make
   

Usage

The pagerank program can be executed with various command line options:

./pagerank [OPTIONS]... [FILENAME]

Options

• -h : Print a brief overview of the available command line parameters.
• -r N : Simulate N steps of the random surfer and output the result.
• -m N : Simulate N steps of the Markov chain and output the result.
• -s : Compute and print the statistics of the graph.
• -p P : Set the probability parameter p for the random surfer model (default is 10%).

Examples

To run a simulation using the random surfer model for 100 steps:

./pagerank -r 100 graph.dot

To run a simulation using the markov chains model with probability(=15):

./pagerank -m -p 15 prog2graph.dot

To display all the different parameter options

./ pagerank -h

Features

• Random Surfer Simulation: Approximates the PageRank by simulating a user's navigation through the network.
• Markov Chain Computation: Provides a more accurate PageRank calculation using matrix multiplication.
• Graph Statistics: Computes and prints statistics about the input graph.

Contributing

Contributions are welcome! Please follow these steps:

• Fork the repository.
• Create a new branch

   git checkout -b feature/your-feature

• Commit your changes

   git commit -m 'Add your feature'

• Push to the branch

   git push origin feature/your-feature

• Open a pull request.