Vertaile menetelmiä

	Bayesiläinen stokastinen lohkomalli	Monikerinen stokastinen lohkomalli
Tieteenala	Verkostoanalyysi	Verkostoanalyysi
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2001–2014	2015-2017
Kehittäjä	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.	Peixoto, T. P.; De Bacco, C. and colleagues
Tyyppi	Probabilistic generative model with Bayesian inference	Generative probabilistic model
Alkuperäislähde	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.
Rinnakkaisnimet	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model	ML-SBM, multilayer SBM, multi-layer stochastic block model, multiplex stochastic block model
Liittyvät	5	4
Tiivistelmä	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.