Compara mètodes

	Model Estocàstic de Blocs Bayesà (Bayesian SBM)	Model Estocàstic de Blocs Multicapa
Camp	Anàlisi de xarxes	Anàlisi de xarxes
Família	Machine learning	Machine learning
Any d'origen	2001–2014	2015-2017
Autor original	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.	Peixoto, T. P.; De Bacco, C. and colleagues
Tipus	Probabilistic generative model with Bayesian inference	Generative probabilistic model
Font seminal	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.
Àlies	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model	ML-SBM, multilayer SBM, multi-layer stochastic block model, multiplex stochastic block model
Relacionats	5	4
Resum	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.