পদ্ধতির তুলনা করুন

	বেইসিয়ান স্টোকাস্টিক ব্লক মডেল	স্টোকাস্টিক ব্লক মডেল
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Machine learning	Process / pipeline
উদ্ভবের বছর	2001–2014	1983
প্রবর্তক	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.	—
ধরন	Probabilistic generative model with Bayesian inference	Probabilistic generative graph model
মৌলিক উৎস	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.
অপর নাম	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
সম্পর্কিত	5	7
সারসংক্ষেপ	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.