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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 176–200 of 393 papers

TitleStatusHype
Multi-View Stochastic Block Models—0
Neighbor Similarity Based Agglomerative Method for Community Detection in Networks—0
Neural-prior stochastic block model—0
A Variational Image Segmentation Model based on Normalized Cut with Adaptive Similarity and Spatial Regularization—0
Notes on Elementary Spectral Graph Theory. Applications to Graph Clustering Using Normalized Cuts—0
On a 'Two Truths' Phenomenon in Spectral Graph Clustering—0
On Learning the Structure of Clusters in Graphs—0
Opinion Holder and Target Extraction based on the Induction of Verbal Categories—0
Learning Optimal Graph Filters for Clustering of Attributed Graphs—0
PageRank and The K-Means Clustering Algorithm—0
PanoGS: Gaussian-based Panoptic Segmentation for 3D Open Vocabulary Scene Understanding—0
Practical Attacks Against Graph-based Clustering—0
Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging—0
Product Graph Learning from Multi-domain Data with Sparsity and Rank Constraints—0
Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification—0
Provable Filter for Real-world Graph Clustering—0
Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed—0
Randomized Algorithms for Symmetric Nonnegative Matrix Factorization—0
Recovering Unbalanced Communities in the Stochastic Block Model With Application to Clustering with a Faulty Oracle—0
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering—0
Resolution-limit-free and local Non-negative Matrix Factorization quality functions for graph clustering—0
Revisiting Spectral Graph Clustering with Generative Community Models—0
Rhomboid Tiling for Geometric Graph Deep Learning—0
Robust Correlation Clustering with Asymmetric Noise—0
Sample-Level Multi-View Graph Clustering—0
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All datasetsCiteseerCoraPubmedBiase et alBozec et alDeng et alGoolam et alPollen et alTreutlein et alYan et al

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1R-DGAEACC70.5—Unverified
2R-GMM-VGAEACC68.9—Unverified
3AGCACC67—Unverified
4RWR-GAEACC61.6—Unverified
5RWR-VGAEACC61.3—Unverified
6ARGEACC57.3—Unverified
7ARVGEACC54.4—Unverified
8GAEACC40.8—Unverified
9DAEGC+GSCAN†NMI39.9—Unverified
#ModelMetricClaimedVerifiedStatus
1R-GMM-VGAEACC76.7—Unverified
2R-DGAEACC73.7—Unverified
3AGCACC68.92—Unverified
4RWR-VGAEACC68.5—Unverified
5RWR-GAEACC66.9—Unverified
6ARGEACC64—Unverified
7ARVGEACC63.8—Unverified
8GAEACC59.6—Unverified
9DAEGC+GSCAN†NMI52.4—Unverified
#ModelMetricClaimedVerifiedStatus
1R-GMM-VGAEACC74—Unverified
2RWR-VGAEACC73.6—Unverified
3RWR-GAEACC72.6—Unverified
4R-DGAEACC71.4—Unverified
5AGCACC69.78—Unverified
6VGAEACC65.48—Unverified
7DAEGC+GSCAN†NMI31.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index1—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.46—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.81—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.81—Unverified