Comparer des méthodes

	Modèle stochastique de blocs temporel	Modèle de blocs stochastiques
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Process / pipeline
Année d'origine	2014–2017	1983
Auteur d'origine	Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V.	—
Type	Generative probabilistic model	Probabilistic generative graph model
Source fondatrice	Matias, C. & Miele, V. (2017). Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4), 1119–1141.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Alias	TSBM, dynamic stochastic block model, time-varying SBM, evolving block model	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Apparentées	4	7
Résumé	The Temporal Stochastic Block Model (TSBM) extends the classic Stochastic Block Model to sequences of network snapshots, jointly inferring latent community memberships and how those memberships evolve across time. It combines a generative edge-probability model with a Markov process over block assignments, enabling principled statistical detection of community structure that changes over time.	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.