#计算机科学#A curated list of community detection research papers with implementations.
翻译 - 精选的社区检测研究论文及其实现。
#计算机科学#Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
翻译 - 通用社区检测和网络嵌入库,用于基于NetworkX构建的研究。
#计算机科学#A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
#计算机科学#Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalabl...
#计算机科学#Deep and conventional community detection related papers, implementations, datasets, and tools.
#人脸识别#Code for our ECCV 2018 work.
#计算机科学#[AAAI 2023] An official source code for paper Hard Sample Aware Network for Contrastive Deep Graph Clustering.
#计算机科学#An implementation of "EdMot: An Edge Enhancement Approach for Motif-aware Community Detection" (KDD 2019)
A NetworkX implementation of Label Propagation from a "Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks" (Physical Review E 2008).
Papers on Graph Analytics, Mining, and Learning
#计算机科学#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
#计算机科学#A NetworkX implementation of "Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters" (KDD 2017).
An implementation of Chinese Whispers in Python.
#计算机科学#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 repr...
Tensorflow and Pytorch implementation of "Just Balance GNN" for graph clustering.
ppSCAN: Parallelizing Pruning-based Graph Structural Clustering (ICPP'18) - by Yulin Che, Shixuan Sun and Prof. Qiong Luo
Prioritizing network communities