Comparer des méthodes

	Modèle de blocs stochastiques bayésien	Analyse de modularité
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2001–2014	2004
Auteur d'origine	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.	Newman, M. E. J. & Girvan, M.
Type	Probabilistic generative model with Bayesian inference	Community detection / graph partitioning
Source fondatrice	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.
Alias	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Apparentées	5	5
Résumé	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.