পদ্ধতির তুলনা করুন

	বেইসিয়ান স্টোকাস্টিক ব্লক মডেল	বহুস্তরীয় স্টোকাস্টিক ব্লক মডেল
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2001–2014	2015-2017
প্রবর্তক	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.	Peixoto, T. P.; De Bacco, C. and colleagues
ধরন	Probabilistic generative model with Bayesian inference	Generative probabilistic 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.	Peixoto, T. P. (2015). Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Physical Review E, 92(4), 042807.
অপর নাম	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model	ML-SBM, multilayer SBM, multi-layer stochastic block model, multiplex stochastic block model
সম্পর্কিত	5	4
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 Multilayer Stochastic Block Model (ML-SBM) is a generative probabilistic framework that extends the classical stochastic block model to networks with multiple relation types or layers. It simultaneously infers community structure and block-to-block connection probabilities across all layers, capturing how communities cohere differently depending on context or relationship type.