Comparer des méthodes

	L'Analyse Bayésienne des Réseaux Sociaux	Modèle de blocs stochastiques
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Process / pipeline
Année d'origine	2002	1983
Auteur d'origine	Hoff, P. D.; Raftery, A. E.; Handcock, M. S.	—
Type	Probabilistic / Bayesian network model	Probabilistic generative graph model
Source fondatrice	Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Alias	Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Apparentées	5	7
Résumé	Bayesian Social Network Analysis applies Bayesian probabilistic inference to relational data, placing prior distributions over network parameters and updating them with observed tie data to yield full posterior distributions over structural features, tie probabilities, and latent actor positions. It enables principled uncertainty quantification in network models, making it especially valuable when data are sparse, partially observed, or subject to measurement error.	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.