Сравнение методов

	Байесовский анализ временных сетей	Bayesian Exponential Random Graph Model
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2010s	2011
Автор метода	Hanneke, S.; Fu, W.; Xing, E. P. (among key contributors)	Caimo, A., & Friel, N.
Тип	Probabilistic generative model	Bayesian statistical model for networks
Основополагающий источник	Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.	Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55.
Другие названия	Bayesian dynamic network analysis, Bayesian time-varying network model, BTNA, Bayesian longitudinal network analysis	Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM
Связанные	4	4
Сводка	Bayesian temporal network analysis combines probabilistic Bayesian inference with time-ordered relational data to model how network structures evolve, quantify uncertainty around structural estimates, and make principled predictions about future connectivity patterns. It provides credible intervals on edge probabilities and community assignments rather than bare point estimates.	The Bayesian Exponential Random Graph Model (Bayesian ERGM or BERGM) extends the classical ERGM framework by placing prior distributions over the model parameters and using Markov chain Monte Carlo methods to obtain full posterior distributions. Introduced by Caimo and Friel (2011), it allows researchers to quantify parameter uncertainty and incorporate prior knowledge when modelling the structural features of social and other complex networks.