Comparer des méthodes

	Détection bayésienne de communautés	Modèle de blocs stochastiques
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Process / pipeline
Année d'origine	2001–2014	1983
Auteur d'origine	Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.	—
Type	Probabilistic generative model / inference	Probabilistic generative graph model
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.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Alias	Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Apparentées	5	7
Résumé	Bayesian community detection infers latent group structure in networks by treating community membership as unobserved variables and using Bayesian inference — typically via Markov chain Monte Carlo or variational methods — to compute a posterior distribution over all plausible partitions. Unlike modularity optimisation, it selects the number of communities from data and provides principled uncertainty estimates for every node assignment.	The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.