Graph Clustering

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.

Source: Clustering for Graph Datasets via Gumbel Softmax

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 11–20 of 393 papers

Title	Date	Tasks	Status	Hype	Score
Accurate Learning of Graph Representations with Graph Multiset Pooling	Feb 23, 2021	Graph ClassificationGraph Clustering	CodeCode Available	1	5
Deep Graph Clustering via Dual Correlation Reduction	Dec 29, 2021	ClusteringFeature Correlation	CodeCode Available	1	5
DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization	Dec 20, 2023	ClusteringGraph Clustering	CodeCode Available	1	5
Deep Attention-guided Graph Clustering with Dual Self-supervision	Nov 10, 2021	ClusteringDeep Attention	CodeCode Available	1	5
Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences	May 30, 2022	ClusteringGraph Clustering	CodeCode Available	1	5
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks	May 20, 2019	ClusteringComputational Efficiency	CodeCode Available	1	5
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource	Nov 23, 2022	AttributeClustering	CodeCode Available	1	5
Attributed Graph Clustering with Dual Redundancy Reduction	Apr 18, 2022	ClusteringContrastive Learning	CodeCode Available	1	5
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection	Mar 13, 2020	ClusteringCo-Salient Object Detection	CodeCode Available	1	5
A Survey of Adversarial Learning on Graphs	Mar 10, 2020	ClusteringGraph Clustering	CodeCode Available	1	5

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All datasets Citeseer Cora Pubmed Biase et al Bozec et al Deng et al Goolam et al Pollen et al Treutlein et al Yan et al

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	R-DGAE	ACC	70.5	—	Unverified
2	R-GMM-VGAE	ACC	68.9	—	Unverified
3	AGC	ACC	67	—	Unverified
4	RWR-GAE	ACC	61.6	—	Unverified
5	RWR-VGAE	ACC	61.3	—	Unverified
6	ARGE	ACC	57.3	—	Unverified
7	ARVGE	ACC	54.4	—	Unverified
8	GAE	ACC	40.8	—	Unverified
9	DAEGC+GSCAN†	NMI	39.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	R-GMM-VGAE	ACC	76.7	—	Unverified
2	R-DGAE	ACC	73.7	—	Unverified
3	AGC	ACC	68.92	—	Unverified
4	RWR-VGAE	ACC	68.5	—	Unverified
5	RWR-GAE	ACC	66.9	—	Unverified
6	ARGE	ACC	64	—	Unverified
7	ARVGE	ACC	63.8	—	Unverified
8	GAE	ACC	59.6	—	Unverified
9	DAEGC+GSCAN†	NMI	52.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	R-GMM-VGAE	ACC	74	—	Unverified
2	RWR-VGAE	ACC	73.6	—	Unverified
3	RWR-GAE	ACC	72.6	—	Unverified
4	R-DGAE	ACC	71.4	—	Unverified
5	AGC	ACC	69.78	—	Unverified
6	VGAE	ACC	65.48	—	Unverified
7	DAEGC+GSCAN†	NMI	31.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Polaratio Consensus Clustering	Adjusted Rand Index	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Polaratio Consensus Clustering	Adjusted Rand Index	0.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Polaratio Consensus Clustering	Adjusted Rand Index	0.46	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Polaratio Consensus Clustering	Adjusted Rand Index	0.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Polaratio Consensus Clustering	Adjusted Rand Index	0.95	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Polaratio Consensus Clustering	Adjusted Rand Index	0.81	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Polaratio Consensus Clustering	Adjusted Rand Index	0.81	—	Unverified