community-detection-algorithm

Some overlapping community detection algorithms (Until 2016). by Yulin Che (https://github.com/CheYulin) for the PhD qualification exam (survey on community detection algorithms)

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

TILES: an algorithm for community discovery in dynamic social networks

Python implementation of Newman's spectral methods to maximize modularity.

DEMON: a local-first discovery method for overlapping communities.

A python script for detecting communities in graphs using the clique percolation method.

Eva: Community Discovery for Labeled Graphs (networkx implementation)

Label propagation algorithm for community detection based on node importance and label influence

A Python implementation of improved Label Propagation Algorithm.

Repository to reproduce "Cascade-based Echo Chamber Detection" accepted at CIKM2022

Code necessary to reproduce the experimentation presented in "A Multi-Objective Genetic Algorithm for Detecting Dynamic Communities using a Local Search driven Immigrant’s Scheme"

APAL: Adjacency Propagation Algorithm for overlapping community detection

DAOC (Deterministic and Agglomerative Overlapping Clustering algorithm): Stable Clustering of Large Networks

CoEuS: Community Detection via Seed-set Expansion on Graph Streams

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.

Exhaustive search for the best Minimally Complex spin Model: "community detection" in binary data.

This project implements the Girvan-Newman and Louvain algorithms from scratch for community detection in graphs, using datasets like Wiki-Vote and LastFM-Social. It includes automated stopping criteria, dendrogram visualization, and performance comparisons.

Reinforcement learning community detection method using known communities

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

This Repository contains series of python programs for simulations of Schelling Model i.e. each node in a grid must satisfy threshold neighbourhood property.