เปรียบเทียบวิธี

	Bayesian Exponential Random Graph Model	การวิเคราะห์มอดูลาริตี
สาขาวิชา	การวิเคราะห์เครือข่าย	การวิเคราะห์เครือข่าย
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2011	2004
ผู้ริเริ่ม	Caimo, A., & Friel, N.	Newman, M. E. J. & Girvan, M.
ประเภท	Bayesian statistical model for networks	Community detection / graph partitioning
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
ที่เกี่ยวข้อง	4	5
สรุป	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.