A NetworkX implementation of "Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters" (KDD 2017).

Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling of Clustering (Community Detection) Algorithms Considering Overlaps (Covers)

Super.Complex is a supervised machine learning algorithm for community detection in networks. It learns information from known communities and uses this information to find new communities on the network.

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.

A summary of the work done for the June 2021 FOSSEE Research Internship in R.

The implementation was done using python networkx and matplot libraries. Zachary karate club dataset is used as a benchmark dataset between the different community detection algorithms. Karate is the well-known and much-used dataset to benchmark algorithms as ground truth of it is two so if a detection algorithm is close to these two sets, then …

Benchmark Generator for Dynamic Overlapping Communities