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 701–750 of 919 papers

TitleStatusHype
Non-Negative Matrix Factorizations for Multiplex Network Analysis—0
Novel Edge and Density Metrics for Link Cohesion—0
OK Boomer: Probing the socio-demographic Divide in Echo Chambers—0
On Consistency of Compressive Spectral Clustering—0
On hyperparameter tuning in general clustering problemsm—0
On labeling Android malware signatures using minhashing and further classification with Structural Equation Models—0
Spectral CUSUM for Online Network Structure Change Detection—0
On-the-Fly Rectification for Robust Large-Vocabulary Topic Inference—0
On the Minimax Misclassification Ratio of Hypergraph Community Detection—0
On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models—0
On the Simultaneous Preservation of Privacy and Community Structure in Anonymized Networks—0
On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization—0
On the use of local structural properties for improving the efficiency of hierarchical community detection methods—0
On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks—0
Optimal Bipartite Network Clustering—0
Optimal Clustering of Discrete Mixtures: Binomial, Poisson, Block Models, and Multi-layer Networks—0
Optimal Cluster Recovery in the Labeled Stochastic Block Model—0
Optimal community detection in dense bipartite graphs—0
Optimal Noise Reduction in Dense Mixed-Membership Stochastic Block Models under Diverging Spiked Eigenvalues Condition—0
Optimal Inference in Contextual Stochastic Block Models—0
Optimal Laplacian regularization for sparse spectral community detection—0
Optimisation dans la détection de communautés recouvrantes et équilibre de Nash—0
Community detection using diffusion informationCode0
A Spectral Analysis of Graph Neural Networks on Dense and Sparse GraphsCode0
Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank-Wolfe ApproachCode0
Testing network clustering algorithms with Natural Language ProcessingCode0
Variational Embeddings for Community Detection and Node RepresentationCode0
CANE: Context-Aware Network Embedding for Relation ModelingCode0
Community Detection with Graph Neural NetworksCode0
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
MGTCOM: Community Detection in Multimodal GraphsCode0
Adaptive Long-term Embedding with Denoising and Augmentation for RecommendationCode0
Community detection with spiking neural networks for neuromorphic hardwareCode0
REM: From Structural Entropy to Community Structure DeceptionCode0
CommunityGAN: Community Detection with Generative Adversarial NetsCode0
Guided Machine Learning for power grid segmentationCode0
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community DetectionCode0
Overlapping Community Detection at Scale: A Nonnegative Matrix Factorization ApproachCode0
Unsupervised Community Detection with Modularity-Based Attention ModelCode0
Hidden Community Detection in Social NetworksCode0
Hide and Seek: Outwitting Community Detection AlgorithmsCode0
Hierarchical Bayesian inference for community detection and connectivity of functional brain networksCode0
Mixed Membership Graph Clustering via Systematic Edge QueryCode0
Community detection over a heterogeneous population of non-aligned networksCode0
Overlapping community detection in networks based on link partitioning and partitioning around medoidsCode0
Overlapping community detection in networks via sparse spectral decompositionCode0
Hierarchical community structure in networksCode0
Time Series Clustering via Community Detection in NetworksCode0
A Spectral Method for Joint Community Detection and Orthogonal Group SynchronizationCode0
CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent EstimationCode0
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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