SOTAVerified

Community Detection

Community Detection is one of the fundamental problems in network analysis, where the goal is to find groups of nodes that are, in some sense, more similar to each other than to the other nodes.

Source: Randomized Spectral Clustering in Large-Scale Stochastic Block Models

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 919 papers

TitleStatusHype
An Effective Index for Truss-based Community Search on Large Directed Graphs—0
Community Detection in the Multi-View Stochastic Block Model—0
Fundamental limits of community detection from multi-view data: multi-layer, dynamic and partially labeled block models—0
Mixture of multilayer stochastic block models for multiview clustering—0
Learning Persistent Community Structures in Dynamic Networks via Topological Data AnalysisCode0
Systematic review of image segmentation using complex networks—0
A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature—0
Benchmarking Evolutionary Community Detection Algorithms in Dynamic Networks—0
Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection—0
A Framework for Exploring Federated Community Detection—0
Uncovering communities of pipelines in the task-fMRI analytical space—0
A Structural-Clustering Based Active Learning for Graph Neural NetworksCode0
Community Detection in High-Dimensional Graph Ensembles—0
Almost Exact Recovery in Gossip Opinion Dynamics over Stochastic Block Models—0
Localizing and Assessing Node Significance in Default Mode Network using Sub-Community Detection in Mild Cognitive Impairment—0
Understanding Opinions Towards Climate Change on Social Media—0
Self-similarity of Communities of the ABCD Model—0
Single-cell Multi-view Clustering via Community Detection with Unknown Number of ClustersCode0
Optimal Clustering of Discrete Mixtures: Binomial, Poisson, Block Models, and Multi-layer Networks—0
Revealing Cortical Layers In Histological Brain Images With Self-Supervised Graph Convolutional Networks Applied To Cell-Graphs—0
Narratives from GPT-derived Networks of News, and a link to Financial Markets Dislocations—0
Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss—0
Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training—0
Unsupervised segmentation of irradiationx2010induced orderx2010disorder phase transitions in electron microscopyCode0
Structure and inference in hypergraphs with node attributesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GNNAccuracy-NE2—Unverified
2CommunityGANF1-score0.09—Unverified
3Ego-SplittingF1-score0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1EdMotNMI0.42—Unverified
2CDNMFNMI0.4—Unverified
#ModelMetricClaimedVerifiedStatus
1CDNMFACC0.48—Unverified
#ModelMetricClaimedVerifiedStatus
1CommunityGANF1-Score0.15—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.56—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.69—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.68—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1CDNMFACC0.67—Unverified