Porovnat metody

	Multilayer Stochastic Block Model	Bayesovský stochastický blokový model
Obor	Analýza sítí	Analýza sítí
Rodina	Machine learning	Machine learning
Rok vzniku	2015-2017	2001–2014
Tvůrce	Peixoto, T. P.; De Bacco, C. and colleagues	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.
Typ	Generative probabilistic model	Probabilistic generative model with Bayesian inference
Původní zdroj	Peixoto, T. P. (2015). Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Physical Review E, 92(4), 042807.	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.
Další názvy	ML-SBM, multilayer SBM, multi-layer stochastic block model, multiplex stochastic block model	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model
Příbuzné	4	5
Shrnutí	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.	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.