PageRank Implementation in Python

This repository contains a Python implementation of the PageRank algorithm using a sparse matrix representation and the power iteration method with a damping factor.

Features

• Sparse Matrix Representation: Constructs the transition matrix as a sparse matrix for memory and computational efficiency.
• Normalization: Ensures each column of the transition matrix sums to 1 (stochastic matrix).
• Power Iteration Method: Computes the PageRank vector using an iterative approach with convergence tolerance.
• Damping Factor: Incorporates the damping factor (set to 0.85) to simulate the behavior of a random surfer.
• Example Usage: Includes a simple example with a network of 6 pages and predefined links.

Algorithm

Requirements

• Python 3.6+
• NumPy
• SciPy

Usage

1. Clone the repository:

   git clone https://github.com/amerob/pagerank-algorithm-python.git
   cd pagerank-algorithm-python

2. Install dependencies:

   pip3 install -r requirements.txt

3. Run the script:

   python main.py

Note

This code only shows the core concepts behind the PageRank algorithm and serves as a starting point for exploring graph ranking. Contributions are welcome.