Bandingkan kaedah

	Model Graf Rawak Eksponen Bayesian	Analisis Modulariti
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2011	2004
Pengasas	Caimo, A., & Friel, N.	Newman, M. E. J. & Girvan, M.
Jenis	Bayesian statistical model for networks	Community detection / graph partitioning
Sumber perintis	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.
Alias	Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Berkaitan	4	5
Ringkasan	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.