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	Bayesowski Model Bloków Stochastycznych	Model blokowy stochastyczny
Dziedzina	Analiza sieci	Analiza sieci
Rodzina	Machine learning	Process / pipeline
Rok powstania	2001–2014	1983
Twórca	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.	—
Typ	Probabilistic generative model with Bayesian inference	Probabilistic generative graph model
Źródło pierwotne	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Inne nazwy	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Pokrewne	5	7
Podsumowanie	The Bayesian Stochastic Block Model (Bayesian SBM) is a principled probabilistic method for community detection in networks. It treats group membership as a latent variable and uses Bayesian inference to simultaneously recover block structure and select the number of communities, avoiding the resolution-limit bias that plagues modularity-based approaches.	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.