Comparer des méthodes

	Modèle stochastique pondéré de blocs	Analyse de modularité pondérée
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2014	2004
Auteur d'origine	Aicher, C.; Jacobs, A. Z.; Clauset, A.	Newman, M. E. J.
Type	Generative probabilistic model	Community structure optimization on weighted graphs
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.	Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131.
Alias	W-SBM, weighted SBM, weighted block model, weighted community detection via SBM	weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity
Apparentées	6	5
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 modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully.