手法を比較

	動的指数型ランダムグラフモデル (TERGM / STERGM)	時間的ネットワーク分析
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Process / pipeline
提唱年	2010–2014	2012
提唱者	Hanneke, Fu & Xing; Krivitsky & Handcock	Holme & Saramäki (2012) — seminal framework
種類	Probabilistic graphical model (temporal)	Dynamic graph analysis
原典	Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
別名	TERGM, Temporal ERGM, Dynamic ERGM, STERGM	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
関連	4	3
概要	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.	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.