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 101125 of 393 papers

TitleStatusHype
Cross-Camera Data Association via GNN for Supervised Graph ClusteringCode0
Correlation Clustering with Same-Cluster Queries Bounded by Optimal CostCode0
Attributed Graph Clustering: A Deep Attentional Embedding ApproachCode0
Correlation Clustering Algorithm for Dynamic Complete Signed Graphs: An Index-based ApproachCode0
Advanced Graph Clustering Methods: A Comprehensive and In-Depth AnalysisCode0
Discrete Optimal Graph ClusteringCode0
Hierarchical Graph Clustering using Node Pair SamplingCode0
Learning Resolution Parameters for Graph ClusteringCode0
Balancing the Tradeoff Between Clustering Value and InterpretabilityCode0
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
An Internal Validity Index Based on Density-Involved DistanceCode0
MeanCut: A Greedy-Optimized Graph Clustering via Path-based Similarity and Degree Descent CriterionCode0
Asymmetric Semi-Nonnegative Matrix Factorization for Directed Graph ClusteringCode0
Hierarchical Position Embedding of Graphs with Landmarks and Clustering for Link PredictionCode0
Gromov-Wasserstein Factorization Models for Graph ClusteringCode0
A Non-negative Symmetric Encoder-Decoder Approach for Community DetectionCode0
Efficient block contrastive learning via parameter-free meta-node approximationCode0
Clustering-based Image-Text Graph Matching for Domain GeneralizationCode0
GSCAN: Graph Stability Clustering for Applications With Noise Using Edge-Aware Excess-of-MassCode0
GraphLearner: Graph Node Clustering with Fully Learnable AugmentationCode0
Non-linear Attributed Graph Clustering by Symmetric NMF with PU LearningCode0
Efficient Multi-View Graph Clustering with Local and Global Structure PreservationCode0
Approximate sampling and estimation of partition functions using neural networksCode0
End-to-End Supervised Hierarchical Graph Clustering for Speaker DiarizationCode0
Asymmetric Semi-Nonnegative Matrix Factorization for Directed Graph ClusteringCode0
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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