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	बायेसियन स्टोकेस्टिक ब्लॉक मॉडल	स्टोकेस्टिक ब्लॉक मॉडल
क्षेत्र	नेटवर्क विश्लेषण	नेटवर्क विश्लेषण
परिवार	Machine learning	Process / pipeline
उद्भव वर्ष	2001–2014	1983
प्रवर्तक	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.	—
प्रकार	Probabilistic generative model with Bayesian inference	Probabilistic generative graph model
मौलिक स्रोत	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.
उपनाम	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
संबंधित	5	7
सारांश	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.