Graph Clustering
146 papers with code • 10 benchmarks • 18 datasets
Graph Clustering is the process of grouping the nodes of the graph into clusters, taking into account the edge structure of the graph in such a way that there are several edges within each cluster and very few between clusters. Graph Clustering intends to partition the nodes in the graph into disjoint groups.
Benchmarks
These leaderboards are used to track progress in Graph Clustering
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Libraries
Use these libraries to find Graph Clustering models and implementationsDatasets
Pubmed
Cora
SNAP
Netzschleuder
97 synthetic datasets
Placenta
Latest papers
MeanCut: A Greedy-Optimized Graph Clustering via Path-based Similarity and Degree Descent Criterion
As the most typical graph clustering method, spectral clustering is popular and attractive due to the remarkable performance, easy implementation, and strong adaptability.
Spectral Clustering of Attributed Multi-relational Graphs
flyingdoog/DMGC
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Many different algorithms have been proposed in the literature: for simple graphs, for graphs with attributes associated to nodes, and for graphs where edges represent different types of relations among nodes.
Robust Graph Clustering via Meta Weighting for Noisy Graphs
We add a learnable weight to each node pair, and MetaGC adaptively adjusts the weights of node pairs using meta-weighting so that the weights of meaningful node pairs increase and the weights of less-meaningful ones (e. g., noise edges) decrease.
Redundancy-Free Self-Supervised Relational Learning for Graph Clustering
yisiyu95/r2fgc
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Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks in recent years.
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views.
Edge-aware Hard Clustering Graph Pooling for Brain Imaging
swfen/ehcpool
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In this paper, we propose a novel edge-aware hard clustering graph pool (EHCPool), which is tailored to dominant edge features and redefines the clustering process.
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
Reinforcement Graph Clustering with Unknown Cluster Number
yueliu1999/awesome-deep-graph-clustering
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To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC).
Homophily-enhanced Structure Learning for Graph Clustering
galogm/hole
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Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results.
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models
google-research/graphworld
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In this work we examine how two additional synthetic graph generators can improve GraphWorld's evaluation; LFR, a well-established model in the graph clustering literature and CABAM, a recent adaptation of the Barabasi-Albert model tailored for GNN benchmarking.
