🕸️ Community Detection in Graphs

A Python project for analyzing network structures and detecting communities using various algorithms such as Louvain, Leiden, Infomap, and others.
The project supports both directed and undirected graphs, computes network statistics, detects communities, and exports comparative evaluation results.

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📁 Project Structure

community-detection/
├── common/             # Core modules for analysis, detection, statistics, visualization
├── run/                # Executable scripts (entry points)
├── data/               # Input graph datasets
├── examples/           # Example usage scripts and visualizations
├── requirements.txt    # Python dependencies
├── README.md           # Project documentation

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Getting Started

1. Create and activate a virtual environment

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2. Run community detection example

python -m run.run_statistics

Or run the full benchmarking with configurable algorithms:

python -m run.run_analysis

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🔧 Features

• Load graphs from edge list text files (directed or undirected)
• Compute network statistics:
  • Node and edge count
  • Largest WCC/SCC components
  • Clustering coefficient
  • Graph density, diameter, and radius
• Community detection algorithms:
  • Louvain
  • Leiden
  • Infomap
  • Walktrap
  • Girvan-Newman
  • Greedy modularity
  • Label Propagation
  • Fast Label Propagation
• Graph visualization (with Matplotlib / NetworkX)
• Export results to CSV for analysis and comparison

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Community Analysis

You can instantiate and run custom analyses as follows:

from common.cd_analysis import CommunityAnalysis

ca = CommunityAnalysis(graph=G)  # or file="data/graph.txt"
df = ca.run(algorithms=["Louvain", "Infomap"])

Supported algorithms:

["Louvain", "Leiden", "Infomap", "Girvan Newman", "Greedy Modularity", "Walktrap", "Label Propagation", "Fast Label Propagation"]

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Community Benchmark

You can run a full benchmark on 100 iterations as follows:

from common.cd_analysis import CommunityBenchmark

ca = CommunityBenchmark(graph=G)  # or file="data/graph.txt"
df = ca.run()
ca.summarize()
ca.plot_all() # plot and export analysis graph

Sample Datasets

Example graph format (cit-Patents.txt):

Node1 Node2
12    14
15    17
...

Each line represents a directed edge in the network.

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Dependencies

Install all required packages from requirements.txt:

Note: Some community detection methods in cdlib may require additional system-level libraries such as graph-tool or infomap.

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Author

Robi Pritržnik (2025)
🔗 pritrznik.si
📧 Contact: robi@pritrznik.si

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License

This project is intended for research and educational purposes only.
Feel free to fork and extend under appropriate attribution.