graph-clustering

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).

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"

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

Code for our ECCV 2018 work.

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

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

Papers on Graph Analytics, Mining, and Learning

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

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

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

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

An implementation of Chinese Whispers in Python.

Awesome graph-level learning methods. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in graph representation learning, graph regression and graph classification to join this project as contribut…

Implementation of "Just Balance GNN" for graph classification and node clustering from the paper "Simplifying Clusterings with Graph Neural Networks".

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

An implementation of the Watset clustering algorithm in Java.