Comparer des méthodes

	Modèle stochastique pondéré de blocs	Analyse des réseaux sociaux pondérés
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2014	2004–2010
Auteur d'origine	Aicher, C.; Jacobs, A. Z.; Clauset, A.	Barrat, A.; Opsahl, T. et al.
Type	Generative probabilistic model	Network analysis framework
Source fondatrice	Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248.	Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.
Alias	W-SBM, weighted SBM, weighted block model, weighted community detection via SBM	Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis
Apparentées	6	6
Résumé	The Weighted Stochastic Block Model (W-SBM) extends the classical stochastic block model to networks whose edges carry numerical weights. By positing that edge weights between node pairs arise from distributions that depend on the block memberships of those nodes, it simultaneously infers a partition of nodes into communities and a set of block-to-block weight parameters — recovering structure invisible to unweighted methods.	Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships.