Porovnať metódy

	Bayesovský stochastický blokový model	Analýza modularity
Odbor	Analýza sietí	Analýza sietí
Rodina	Machine learning	Machine learning
Rok vzniku	2001–2014	2004
Tvorca	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.	Newman, M. E. J. & Girvan, M.
Typ	Probabilistic generative model with Bayesian inference	Community detection / graph partitioning
Pôvodný zdroj	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Ďalšie názvy	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Príbuzné	5	5
Zhrnutie	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.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.