Process / pipeline
Stochastic Block Model — Probabilistic Community Detection in Networks
The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.
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Sources
- Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI: 10.1016/0378-8733(83)90021-7 ↗
- Lee, C. & Wilkinson, D.J. (2019). A Review of Stochastic Block Models and Extensions for Graph Clustering. Applied Network Science, 4(1), 122. DOI: 10.1007/s41109-019-0232-2 ↗
Related methods
Referenced by
Bayesian Community DetectionBayesian Exponential Random Graph ModelBayesian Multiplex Network AnalysisBayesian Social Network AnalysisBayesian Stochastic Block ModelCentrality AnalysisCommunity DetectionDirected Community DetectionDirected Exponential Random Graph ModelDirected Modularity AnalysisDynamic Community DetectionDynamic Exponential Random Graph ModelDynamic Stochastic Block ModelLink PredictionMultilayer Community DetectionMultilayer Stochastic Block ModelSmall-World and Scale-Free Network AnalysisTemporal Stochastic Block ModelWeighted Stochastic Block Model