방법 비교

	동적 무향 그래프 모델 (Dynamic Exponential Random Graph Model)	확률적 블록 모형 (Stochastic Block Model, SBM)
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Process / pipeline
기원 연도	2010–2014	1983
창시자	Hanneke, Fu & Xing; Krivitsky & Handcock	—
유형	Probabilistic graphical model (temporal)	Probabilistic generative graph model
원전	Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
별칭	TERGM, Temporal ERGM, Dynamic ERGM, STERGM	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
관련	4	7
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 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.