mlsanityData to 7 Oct 2026

Community Detection in Degree-Corrected Block Models

arXiv 1607.06993 ↗First posted 24 Jul 2016math.ST cs.SI stat.MLcited by 36

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Abstract (arXiv)

Community detection is a central problem of network data analysis. Given a network, the goal of community detection is to partition the network nodes into a small number of clusters, which could often help reveal interesting structures. The present paper studies community detection in Degree-Corrected Block Models (DCBMs). We first derive asymptotic minimax risks of the problem for a misclassification proportion loss under appropriate conditions. The minimax risks are shown to depend on degree-correction parameters, community sizes, and average within and between community connectivities in an intuitive and interpretable way. In addition, we propose a polynomial time algorithm to adaptively perform consistent and even asymptotically optimal community detection in DCBMs.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jul 2016.

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Citing papers per year: 2021: 7; 2022: 4; 2023: 8; 2024: 4; 2025: 8; 2026: 5.

36 papers.

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  1. 2021An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guaranteescited by 10
  2. 2021Community detection for weighted bipartite networkscited by 8
  3. 2023Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detectioncited by 8
  4. 2021Spectral clustering under degree heterogeneity: a case for the random walk Laplaciancited by 6
  5. 2022Optimal Clustering by Lloyd Algorithm for Low-Rank Mixture Modelcited by 5
  6. 2023Efficiently matching random inhomogeneous graphs via degree profilescited by 5
  7. 2021Nonparametric Modeling of Higher-Order Interactions via Hypergraphonscited by 4
  8. 2022Multiway Spherical Clustering via Degree-Corrected Tensor Block Modelscited by 4
  9. 2022The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networkscited by 4
  10. 2023Adversarially robust clustering with optimality guaranteescited by 4
  11. 2024Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Modelscited by 3
  12. 2021Exact Recovery of Community Structures Using DeepWalk and Node2veccited by 2
24 more
  1. 2023Semi-supervised Community Detection via Structural Similarity Metricscited by 2
  2. 2021Community detection in censored hypergraphcited by 1
  3. 2022Higher-order accurate two-sample network inference and network hashingcited by 1
  4. 2023rMultiNet: An R Package For Multilayer Networks Analysiscited by 1
  5. 2023Of Spiky SVDs and Music Recommendationcited by 1
  6. 2024Signed Diverse Multiplex Networks: Clustering and Inferencecited by 1
  7. 2025Optimal community detection in dense bipartite graphscited by 1
  8. 2025Strongly Consistent Community Detection in Popularity Adjusted Block Modelscited by 1
  9. 2026Matrix Factorization Framework for Community Detection under the Degree-Corrected Block Modelcited by 1
  10. 2021Block Dense Weighted Networks with Augmented Degree Correctioncited by 0
  11. 2023Global Minima, Recoverability Thresholds, and Higher-Order Structure in GNNScited by 0
  12. 2023Community Detection Guarantees Using Embeddings Learned by Node2Veccited by 0
  13. 2024VEC-SBM: Optimal Community Detection with Vectorial Edges Covariatescited by 0
  14. 2024Selecting the Number of Communities for Weighted Degree-Corrected Stochastic Block Modelscited by 0
  15. 2025Community Detection for Contextual-LSBM: Theoretical Limitations of Misclassification Rate and Efficient Algorithmscited by 0
  16. 2025Signal Recovery from Random Dot-Product Graphs Under Local Differential Privacycited by 0
  17. 2025Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architecturescited by 0
  18. 2025Graph Neural Networks Powered by Encoder Embedding for Improved Node Learningcited by 0
  19. 2025Information Loss and Disparate Effects in Network Embeddingscited by 0
  20. 2025Optimal Graph Clustering without Edge Density Signalscited by 0
  21. 2026Adjacency Spectral Embeddings of Correlation Networkscited by 0
  22. 2026Individual-heterogeneous sub-Gaussian Mixture Modelscited by 0
  23. 2026One-Bit Clustering for Two Component Sub-Gaussian Mixture Modelscited by 0
  24. 2026Spectral graph clustering with inhomogeneous latent geometrycited by 0