Bandingkan metode

	Model Graf Eksponensial Acak Dinamis	Model Blok Stokastik Dinamis
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2010–2014	2011
Pencetus	Hanneke, Fu & Xing; Krivitsky & Handcock	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.
Tipe	Probabilistic graphical model (temporal)	Generative probabilistic model
Sumber perintis	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
Terkait	4	5
Ringkasan	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.