방법 비교

	동적 무향 그래프 모델 (Dynamic Exponential Random Graph Model)	네트워크 확산 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2010–2014	1927 (epidemic roots); network formalization 1990s–2000s
창시자	Hanneke, Fu & Xing; Krivitsky & Handcock	Kermack, W. O. & McKendrick, A. G.
유형	Probabilistic graphical model (temporal)	Simulation / analytical model
원전	Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.	Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721.
별칭	TERGM, Temporal ERGM, Dynamic ERGM, STERGM	diffusion on networks, information diffusion, contagion spreading model, network propagation 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.	Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally.