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 326–350 of 919 papers

TitleStatusHype
Ensemble approaches for improving community detection methods—0
Community-Aware Graph Signal Processing—0
Entropic Spectral Learning for Large-Scale Graphs—0
Estimating mixed memberships in multi-layer networks—0
Estimating Mixed-Memberships Using the Symmetric Laplacian Inverse Matrix—0
Fast Community Detection based on Graph Autoencoder Reconstruction—0
Consistency of regularized spectral clustering in degree-corrected mixed membership model—0
Estimating the number of communities in networks by spectral methods—0
Community Detection Guarantees Using Embeddings Learned by Node2Vec—0
Estimation of Number of Communities in Assortative Sparse Networks—0
Evaluating accuracy of community detection using the relative normalized mutual information—0
Evaluating Community Detection Algorithms for Progressively Evolving Graphs—0
An _p theory of PCA and spectral clustering—0
Community Detection and Matrix Completion with Social and Item Similarity Graphs—0
Evaluating the effect of topic consideration in identifying communities of rating-based social networks—0
Evidential community detection based on density peaks—0
Evidential Label Propagation Algorithm for Graphs—0
Evolutionary Multi Objective Optimization Algorithm for Community Detection in Complex Social Networks—0
Exact Matching in Correlated Networks with Node Attributes for Improved Community Recovery—0
Optimal and exact recovery on general non-uniform Hypergraph Stochastic Block Model—0
Exact Recovery of Community Detection in dependent Gaussian Mixture Models—0
Exact Recovery of Community Detection in k-Community Gaussian Mixture Model—0
EXIT Analysis for Community Detection—0
Deep Amortized Relational Model with Group-Wise Hierarchical Generative Process—0
AceKG: A Large-scale Knowledge Graph for Academic Data Mining—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