GiulioRossetti/cdlib

What it solves

CDlib addresses the difficulty of implementing and comparing different community detection algorithms in complex networks. It provides a unified interface for extracting, comparing, and evaluating communities, removing the need for users to manually switch between different library implementations or handle inconsistent data formats.

How it works

CDlib acts as a meta-library that implements various community detection algorithms and clustering fitness functions. It is built around the networkx library but can automatically convert data between networkx and igraph objects to ensure compatibility across different algorithm implementations. To maintain accuracy and consistency with academic literature, it often inherits implementations directly from original projects.

Who it’s for

This library is designed for researchers and data scientists working with complex networks and graph analysis who need a tool to discover and evaluate community structures within those networks.

Highlights

• Standardized input and output facilities for multiple community discovery algorithms.
• Built-in clustering fitness functions for evaluation.
• Integrated visualization facilities for network communities.
• Seamless integration with networkx and igraph.