Comparer des méthodes

	Modèle dynamique de graphe aléatoire exponentiel	Modèle dynamique stochastique par blocs
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2010–2014	2011
Auteur d'origine	Hanneke, Fu & Xing; Krivitsky & Handcock	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.
Type	Probabilistic graphical model (temporal)	Generative probabilistic model
Source fondatrice	Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.	Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 82(2), 157–189.
Alias	TERGM, Temporal ERGM, Dynamic ERGM, STERGM	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model
Apparentées	4	5
Résumé	The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about longitudinal network change.	The Dynamic Stochastic Block Model (DSBM) is a generative probabilistic framework that extends the static stochastic block model to networks observed across multiple time points. It jointly models community membership and community evolution, allowing researchers to detect and track latent groups and their structural changes over time in longitudinal network data.