A curated list of community detection research papers with implementations.

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

Code for our ECCV 2018 work.

Deep and conventional community detection related papers, implementations, datasets, and tools.

Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering"

A NetworkX implementation of Label Propagation from a "Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks" (Physical Review E 2008).

[AAAI 2023] An official source code for paper Hard Sample Aware Network for Contrastive Deep Graph Clustering.

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

Papers on Graph Analytics, Mining, and Learning

An implementation of "EdMot: An Edge Enhancement Approach for Motif-aware Community Detection" (KDD 2019)

ppSCAN: Parallelizing Pruning-based Graph Structural Clustering (ICPP'18) - by Yulin Che, Shixuan Sun and Prof. Qiong Luo

Fast consensus clustering in networks

An implementation of Chinese Whispers in Python.

MCL, the Markov Cluster algorithm, also known as Markov Clustering, is a method and program for clustering weighted or simple networks, a.k.a. graphs.

WWW2020-One2Multi Graph Autoencoder for Multi-view Graph Clustering

This project is a scalable unified framework for deep graph clustering.

A list of data mining and machine learning papers that I implemented in 2019.

A set of notebooks related to convex optimization, variational inference and numerical methods for signal processing, machine learning, deep learning, graph analysis, bayesian programming, statistics or astronomy.