Confronta i metodi

	Modello Stocastico a Blocchi Pesato	Rilevamento di Comunità Ponderato
Campo	Analisi delle reti	Analisi delle reti
Famiglia	Machine learning	Machine learning
Anno di origine	2014	2004–2008
Ideatore	Aicher, C.; Jacobs, A. Z.; Clauset, A.	Newman, M. E. J.; Blondel et al.
Tipo	Generative probabilistic model	Graph clustering / community detection
Fonte seminale	Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248.	Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
Alias	W-SBM, weighted SBM, weighted block model, weighted community detection via SBM	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
Correlati	6	6
Sintesi	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 community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation.