SOTAVerified

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

Showing 126150 of 393 papers

TitleStatusHype
Ensemble Clustering for Graphs: Comparisons and ApplicationsCode0
Asymmetric Semi-Nonnegative Matrix Factorization for Directed Graph ClusteringCode0
Memetic Graph ClusteringCode0
Pruned Neural Networks are Surprisingly ModularCode0
Comparison and Benchmark of Graph Clustering AlgorithmsCode0
Learning Networks from Random Walk-Based Node SimilaritiesCode0
A Streaming Algorithm for Graph ClusteringCode0
Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and BetterCode0
RDSA: A Robust Deep Graph Clustering Framework via Dual Soft AssignmentCode0
Learning Persistent Community Structures in Dynamic Networks via Topological Data AnalysisCode0
Latent structure blockmodels for Bayesian spectral graph clusteringCode0
A Semidefinite Relaxation Approach for Fair Graph ClusteringCode0
arXiv4TGC: Large-Scale Datasets for Temporal Graph ClusteringCode0
Inferring Networks From Random Walk-Based Node SimilaritiesCode0
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified ModelsCode0
Learning Resolution Parameters for Graph ClusteringCode0
Hierarchical Position Embedding of Graphs with Landmarks and Clustering for Link PredictionCode0
Hierarchical Graph Clustering using Node Pair SamplingCode0
Hippocluster: an efficient, hippocampus-inspired algorithm for graph clusteringCode0
EGRC-Net: Embedding-induced Graph Refinement Clustering NetworkCode0
Calibrating and Improving Graph Contrastive LearningCode0
Gromov-Wasserstein Factorization Models for Graph ClusteringCode0
GSCAN: Graph Stability Clustering for Applications With Noise Using Edge-Aware Excess-of-MassCode0
Feudal Graph Reinforcement LearningCode0
A Projection Method for Metric-Constrained OptimizationCode0
Show:102550
← PrevPage 6 of 16Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1R-DGAEACC70.5Unverified
2R-GMM-VGAEACC68.9Unverified
3AGCACC67Unverified
4RWR-GAEACC61.6Unverified
5RWR-VGAEACC61.3Unverified
6ARGEACC57.3Unverified
7ARVGEACC54.4Unverified
8GAEACC40.8Unverified
9DAEGC+GSCAN†NMI39.9Unverified
#ModelMetricClaimedVerifiedStatus
1R-GMM-VGAEACC76.7Unverified
2R-DGAEACC73.7Unverified
3AGCACC68.92Unverified
4RWR-VGAEACC68.5Unverified
5RWR-GAEACC66.9Unverified
6ARGEACC64Unverified
7ARVGEACC63.8Unverified
8GAEACC59.6Unverified
9DAEGC+GSCAN†NMI52.4Unverified
#ModelMetricClaimedVerifiedStatus
1R-GMM-VGAEACC74Unverified
2RWR-VGAEACC73.6Unverified
3RWR-GAEACC72.6Unverified
4R-DGAEACC71.4Unverified
5AGCACC69.78Unverified
6VGAEACC65.48Unverified
7DAEGC+GSCAN†NMI31.7Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index1Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.57Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.46Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.91Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.95Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.81Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.81Unverified