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 251300 of 393 papers

TitleStatusHype
flow-based clustering and spectral clustering: a comparison0
G^2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering0
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction0
GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification0
GoWvis: A Web Application for Graph-of-Words-based Text Visualization and Summarization0
Graph Clustering Bandits for Recommendation0
Graph Clustering: Block-models and model free results0
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images0
Graph clustering with Boltzmann machines0
Graph Clustering with Cross-View Feature Propagation0
Graph Clustering with Graph Neural Networks0
Graph Clustering With Missing Data: Convex Algorithms and Analysis0
Graph learning methods to extract empathy supporting regions in a naturalistic stimuli fMRI0
Graphons, mergeons, and so on!0
Graphs in machine learning: an introduction0
HAECcity: Open-Vocabulary Scene Understanding of City-Scale Point Clouds with Superpoint Graph Clustering0
Hermitian matrices for clustering directed graphs: insights and applications0
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time0
Higher-Order Spectral Clustering of Directed Graphs0
Human Semantic Segmentation using Millimeter-Wave Radar Sparse Point Clouds0
Hybrid Clustering based on Content and Connection Structure using Joint Nonnegative Matrix Factorization0
Hyperedge Modeling in Hypergraph Neural Networks by using Densest Overlapping Subgraphs0
Improved Dual Correlation Reduction Network0
Improved Graph Clustering0
Incorporating Higher-order Structural Information for Graph Clustering0
Incremental Multi-graph Matching via Diversity and Randomness based Graph Clustering0
Independence Promoted Graph Disentangled Networks0
Influence-Based Mini-Batching for Graph Neural Networks0
Information Recovery in Shuffled Graphs via Graph Matching0
Integration of graph clustering with ant colony optimization for feature selection0
JoBimText Visualizer: A Graph-based Approach to Contextualizing Distributional Similarity0
Feudal Graph Reinforcement LearningCode0
arXiv4TGC: Large-Scale Datasets for Temporal Graph ClusteringCode0
Faster Approximation Algorithms for Parameterized Graph Clustering and Edge LabelingCode0
A Projection Method for Metric-Constrained OptimizationCode0
Multi-order Graph Clustering with Adaptive Node-level Weight LearningCode0
A Non-negative Symmetric Encoder-Decoder Approach for Community DetectionCode0
An Internal Validity Index Based on Density-Involved DistanceCode0
CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data AssociationCode0
An ensemble based on a bi-objective evolutionary spectral algorithm for graph clusteringCode0
EGRC-Net: Embedding-induced Graph Refinement Clustering NetworkCode0
Ensemble Clustering for Graphs: Comparisons and ApplicationsCode0
Affinity Clustering: Hierarchical Clustering at ScaleCode0
Pruned Neural Networks are Surprisingly ModularCode0
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural NetworksCode0
Non-linear Attributed Graph Clustering by Symmetric NMF with PU LearningCode0
When Slepian Meets Fiedler: Putting a Focus on the Graph SpectrumCode0
Self-Supervised Metric Learning With Graph Clustering For Speaker DiarizationCode0
Supervised Hierarchical Clustering using Graph Neural Networks for Speaker DiarizationCode0
Graph Fuzzy System: Concepts, Models and AlgorithmsCode0
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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