Linganisha mbinu

	Dynamic Exponential Random Graph Model	Aina kuu ya Kielelezo Sanifu cha Kizuizi cha Kielektroniki (DSBM)
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2010–2014	2011
Mwanzilishi	Hanneke, Fu & Xing; Krivitsky & Handcock	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.
Aina	Probabilistic graphical model (temporal)	Generative probabilistic model
Chanzo asilia	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.
Majina mbadala	TERGM, Temporal ERGM, Dynamic ERGM, STERGM	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model
Zinazohusiana	4	5
Muhtasari	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.