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

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Showing 476–500 of 919 papers

TitleStatusHype
Distributed Representation of Subgraphs—0
Distribution-Free Model for Community Detection—0
Disunited Nations? A Multiplex Network Approach to Detecting Preference Affinity Blocs using Texts and Votes—0
Diversified Top-k Partial MaxSAT Solving—0
Diversity of meso-scale architecture in human and non-human connectomes—0
Down the Rabbit Hole: Detecting Online Extremism, Radicalisation, and Politicised Hate Speech—0
Dual regularized Laplacian spectral clustering methods on community detection—0
Dynamic change-point detection using similarity networks—0
Dynamic Community Detection into Analyzing of Wildfires Events—0
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning—0
Dynamic Network Sampling for Community Detection—0
Dynamic Range in the C.elegans Brain Network—0
Early Detection of Research Trends—0
Early Identification of Pathogenic Social Media Accounts—0
Edge Augmentation on Disconnected Graphs via Eigenvalue Elevation—0
Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results—0
Effective Resistance-based Germination of Seed Sets for Community Detection—0
Efficiently Detecting Overlapping Communities through Seeding and Semi-Supervised Learning—0
Efficient Minimax Signal Detection on Graphs—0
Efficient Personalized Community Detection via Genetic Evolution—0
Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms—0
Enhancing Scalability of Optimal Kron-based Reduction of Networks (Opti-KRON) via Decomposition with Community Detection—0
Ensemble approaches for improving community detection methods—0
Entropic Spectral Learning for Large-Scale Graphs—0
Estimating mixed memberships in multi-layer networks—0
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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