Comparer des méthodes

	Centralité de Besoins Bayésienne	L'Analyse Bayésienne des Réseaux Sociaux
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2010s	2002
Auteur d'origine	Brandes, U. (betweenness); Bayesian extension developed by multiple authors (2010s)	Hoff, P. D.; Raftery, A. E.; Handcock, M. S.
Type	Probabilistic network centrality measure	Probabilistic / Bayesian network model
Source fondatrice	Newman, M.E.J. (2010). Networks: An Introduction. Oxford University Press. ISBN: 978-0-19-920665-0	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.
Alias	Bayesian BC, probabilistic betweenness centrality, uncertainty-aware betweenness centrality, posterior betweenness estimation	Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling
Apparentées	3	5
Résumé	Bayesian Betweenness Centrality estimates how often a node lies on shortest paths in a network while explicitly quantifying uncertainty arising from incomplete, sampled, or noisy edge observations. Rather than producing a single point estimate, it yields a posterior distribution over betweenness scores, enabling credible intervals and probabilistic comparisons between nodes.	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.