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 81–90 of 393 papers

Title	Date	Tasks	Status	Hype
Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis	Jan 6, 2024	ClusteringCommunity Detection	CodeCode Available	0
Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering	Jan 5, 2024	ClusteringGraph Clustering	CodeCode Available	1
Efficient High-Quality Clustering for Large Bipartite Graphs	Dec 28, 2023	ClusteringGraph Clustering	CodeCode Available	0
DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization	Dec 20, 2023	ClusteringGraph Clustering	CodeCode Available	1
VSR-Net: Vessel-like Structure Rehabilitation Network with Graph Clustering	Dec 20, 2023	ClusteringGraph Clustering	—Unverified	0
A low-rank non-convex norm method for multiview graph clustering	Dec 18, 2023	ClusteringGraph Clustering	—Unverified	0
MeanCut: A Greedy-Optimized Graph Clustering via Path-based Similarity and Degree Descent Criterion	Dec 7, 2023	ClusteringFace Recognition	CodeCode Available	0
Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering	Nov 23, 2023	ClusteringContrastive Learning	—Unverified	0
The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change	Nov 21, 2023	ClusteringGraph Clustering	—Unverified	0
Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training	Nov 18, 2023	Community DetectionContrastive Learning	—Unverified	0

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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