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 351–360 of 393 papers

Title	Date	Tasks	Status
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching	Sep 10, 2018	ClusteringCombinatorial Optimization	CodeCode Available
Random projection tree similarity metric for SpectralNet	Feb 25, 2023	ClusteringGraph Clustering	CodeCode Available
Real-time Trajectory-based Social Group Detection	Apr 12, 2023	Graph ClusteringRobot Navigation	CodeCode Available
Learning Networks from Random Walk-Based Node Similarities	Jan 23, 2018	Anomaly DetectionClustering	CodeCode Available
Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis	Jan 6, 2024	ClusteringCommunity Detection	CodeCode Available
Learning Resolution Parameters for Graph Clustering	Mar 12, 2019	ClusteringGraph Clustering	CodeCode Available
SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distance	Mar 24, 2022	ClusteringData Augmentation	CodeCode Available
The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks	Oct 2, 2023	ClusteringCommunity Detection	CodeCode Available
Local Algorithms for Finding Densely Connected Clusters	Jun 9, 2021	ClusteringGraph Clustering	CodeCode Available
Local Clustering for Lung Cancer Image Classification via Sparse Solution Technique	Jul 11, 2024	ClusteringGraph Clustering	CodeCode Available

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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