Advanced Graph Clustering method documentation and implementation (From Spectral Clustering to Deep Graph Clustering)

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.

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

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"

Julia package to Compare Graph Embeddings

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)

Codebase for graph clustering using GNN

An implementation of Chinese Whispers in Python.

Pytorch and Tensorflow implementation of TVGNN, presented at ICML 2023.

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.

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

The source code of paper "Shared-Attribute Multi-Graph Clustering with Global Self-Attention"

We present a new rank-based metric for embedding based Knowledge Graph Completion models.

[Accepted by TNNLS] Source Code for Relational Redundancy-Free Graph Clustering

[TKDD'23] Demo code of the paper entitled "Towards a Better Trade-Off between Quality and Efficiency of Community Detection: An Inductive Embedding Method across Graphs", which has been accepted by ACM TKDD

Mining from git history with Pink Pony!

Graph Clustering

Self-contrastive graph diffusion network

Implementation of Graph pooling and clustering operation using Graph Neural Networks in PyTorch