Comparer des méthodes

	Détection bayésienne de communautés	Analyse des réseaux sociaux
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2001–2014	1934 (sociometry); 1994 (modern formalization)
Auteur d'origine	Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.	Moreno, J.L.; formalized by Wasserman & Faust
Type	Probabilistic generative model / inference	Structural/relational analysis framework
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.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Alias	Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning	SNA, network analysis, sociometric analysis, relational analysis
Apparentées	5	5
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.	Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system.