手法を比較

	ベイジアン時系列ネットワーク分析	ベイズ的確率的ブロックモデル
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Machine learning
提唱年	2010s	2001–2014
提唱者	Hanneke, S.; Fu, W.; Xing, E. P. (among key contributors)	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.
種類	Probabilistic generative model	Probabilistic generative model with Bayesian inference
原典	Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.
別名	Bayesian dynamic network analysis, Bayesian time-varying network model, BTNA, Bayesian longitudinal network analysis	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model
関連	4	5
概要	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 Stochastic Block Model (Bayesian SBM) is a principled probabilistic method for community detection in networks. It treats group membership as a latent variable and uses Bayesian inference to simultaneously recover block structure and select the number of communities, avoiding the resolution-limit bias that plagues modularity-based approaches.