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 801–825 of 919 papers

TitleStatusHype
Active Learning for Community Detection in Stochastic Block Models—0
Evaluating the effect of topic consideration in identifying communities of rating-based social networks—0
Weighted Spectral Cluster Ensemble—0
Community Detection with Node Attributes and its Generalization—0
Network structure, metadata and the prediction of missing nodes and annotations—0
Performance of a community detection algorithm based on semidefinite programming—0
On the Simultaneous Preservation of Privacy and Community Structure in Anonymized Networks—0
Inference via Message Passing on Partially Labeled Stochastic Block Models—0
Recovering Structured Probability Matrices—0
Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space—0
Partial Recovery Bounds for the Sparse Stochastic Block Model—0
Hierarchical organization of functional connectivity in the mouse brain: a complex network approach—0
A new correlation clustering method for cancer mutation analysis—0
Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means—0
Real-Time Community Detection in Large Social Networks on a LaptopCode0
Detecting local processing unit in drosophila brain by using network theory—0
Dynamic Range in the C.elegans Brain Network—0
Convexified Modularity Maximization for Degree-corrected Stochastic Block Models—0
Incremental Method for Spectral Clustering of Increasing Orders—0
Topic segmentation via community detection in complex networks—0
Community Detection via Measure Space Embedding—0
An Empirical Comparison of the Summarization Power of Graph Clustering Methods—0
Hierarchical Latent Semantic Mapping for Automated Topic Generation—0
Learning Communities in the Presence of Errors—0
Statistical physics of inference: Thresholds and algorithmsCode0
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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