Comparer des méthodes

	Détection de communautés	Modèle de blocs stochastiques
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Process / pipeline	Process / pipeline
Année d'origine	2002–2019 (algorithm family)	1983
Auteur d'origine	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	—
Type	Graph-partitioning / clustering algorithm family	Probabilistic generative graph model
Source fondatrice	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Alias	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Apparentées	5	7
Résumé	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?	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.