방법 비교

	동적 무향 그래프 모델 (Dynamic Exponential Random Graph Model)	동적 확률 블록 모형
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2010–2014	2011
창시자	Hanneke, Fu & Xing; Krivitsky & Handcock	Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.
유형	Probabilistic graphical model (temporal)	Generative probabilistic model
원전	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.
별칭	TERGM, Temporal ERGM, Dynamic ERGM, STERGM	DSBM, dynamic SBM, time-varying stochastic block model, temporal block model
관련	4	5
요약	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.