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 301–325 of 919 papers

TitleStatusHype
A Unified Framework for Tuning Hyperparameters in Clustering Problems—0
A Local Perspective-based Model for Overlapping Community Detection—0
Active Learning for Community Detection in Stochastic Block Models—0
Accelerating Hybrid Agent-Based Models and Fuzzy Cognitive Maps: How to Combine Agents who Think Alike?—0
Heterogeneous-Temporal Graph Convolutional Networks: Make the Community Detection Much Better—0
Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition—0
Community Detection on Evolving Graphs—0
Community detection in weighted brain connectivity networks beyond the resolution limit—0
A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy—0
Almost Exact Recovery in Gossip Opinion Dynamics over Stochastic Block Models—0
Active Community Detection with Maximal Expected Model Change—0
Community detection in the sparse hypergraph stochastic block model—0
A testing based extraction algorithm for identifying significant communities in networks—0
Community Detection in the Multi-View Stochastic Block Model—0
Community Detection in the Hypergraph SBM: Exact Recovery Given the Similarity Matrix—0
A Tensor Approach to Learning Mixed Membership Community Models—0
A likelihood-ratio type test for stochastic block models with bounded degrees—0
Community Detection in Sparse Random Networks—0
A Survey on Theoretical Advances of Community Detection in Networks—0
Community detection in sparse latent space models—0
Community Detection in Political Twitter Networks using Nonnegative Matrix Factorization Methods—0
A Survey on Signed Graph Embedding: Methods and Applications—0
Algorithms for item categorization based on ordinal ranking data—0
A cost-based multi-layer network approach for the discovery of patient phenotypes—0
A Boosting Approach to Learning Graph Representations—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