There are 28 repositories under graph-clustering topic.
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)
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 PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
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).
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…
Official implementation of our paper "Contrastive Deep Nonnegative Matrix Factorization for Community Detection" (ICASSP 2024)
Implementation of "Just Balance GNN" for graph classification and node clustering from the paper "Simplifying Clusterings with Graph Neural Networks".
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.
Prioritizing network communities
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.
Graph matching and clustering by comparing heat kernels via optimal transport.
Graph Agglomerative Clustering Library
Fast consensus clustering in networks
A list of data mining and machine learning papers that I implemented in 2019.
Pytorch (PyG) and Tensorflow (Keras/Spektral) implementation of Total Variation Graph Neural Network (TVGNN), as presented at ICML 2023.
[KDD 2024] Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Community detection using attribute and structural similarities.
Graph clustering and Node embeddings with word2vec
Implementation of Force2Vec method for ICDM 2020 paper titled "Force2Vec: Parallel force-directed graph embedding"
PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks"
Graph clustering based on dynamic Ollivier-Ricci curvature.