Extensive Benchmarking of community detection algorithms

Description

This project contains analysis and results from artificial and social benchmark analysis.

Benchmark Workflow

Folder Structure

• Figures/: Contains the figures used in the project.
• LFR_Results/: Contains results from artificial benchmark analysis.
• Social_benchmark_results/: Contains results from social benchmark analysis.
• Social_networks/: Contains datasets used for benchmarking.
• LFR_networks/: Folders containing LFR networks used for benchmarking.
• ppi_data/:Directory containing the data and results from gene enrichment analysis
• community_detection/: Contains the Python module developed for implementing community detection algorithms. This directory also includes a README file with detailed information about the package.

Usage

To reobtain the figures, run the following Jupyter notebooks:

• LFR_Results.ipynb
• Social_Results.ipynb
• Estimating_mu.ipynb
• Gene_enrichment.ipynb

Installation

To install the Python module for community detection, navigate to the community_detection directory and run:

pip install .