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 101–125 of 393 papers

TitleStatusHype
Hyperedge Modeling in Hypergraph Neural Networks by using Densest Overlapping Subgraphs—0
Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation—0
Axioms for graph clustering quality functions—0
DEEP GEOMETRICAL GRAPH CLASSIFICATION—0
An application of topological graph clustering to protein function prediction—0
Deep Cut-informed Graph Embedding and Clustering—0
Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation—0
Distributed Graph Clustering by Load Balancing—0
Do logarithmic proximity measures outperform plain ones in graph clustering?—0
Attributed Graph Clustering in Collaborative Settings—0
Dual Information Enhanced Multi-view Attributed Graph Clustering—0
Dual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering—0
Dynamic Joint Variational Graph Autoencoders—0
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images—0
Graph Clustering Bandits for Recommendation—0
Effective and Scalable Clustering on Massive Attributed Graphs—0
A Non-negative Symmetric Encoder-Decoder Approach for Community Detection—0
Efficient Eigen-updating for Spectral Graph Clustering—0
GoWvis: A Web Application for Graph-of-Words-based Text Visualization and Summarization—0
Capacity Releasing Diffusion for Speed and Locality.—0
Efficient model selection in switching linear dynamic systems by graph clustering—0
Capacity Releasing Diffusion for Speed and Locality—0
Graph Clustering: Block-models and model free results—0
Embedding Graph Auto-Encoder for Graph Clustering—0
Graph clustering with Boltzmann machines—0
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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