手法を比較

	ベイズ的指数型ランダムグラフモデル	モジュラリティ分析
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	2011	2004
提唱者	Caimo, A., & Friel, N.	Newman, M. E. J. & Girvan, M.
種類	Bayesian statistical model for networks	Community detection / graph partitioning
原典	Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
別名	Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
関連	4	5
概要	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.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.