เปรียบเทียบวิธี

	แบบจำลองบล็อกสุ่มแบบเบย์ (Bayesian Stochastic Block Model)	การตรวจจับชุมชน
สาขาวิชา	การวิเคราะห์เครือข่าย	การวิเคราะห์เครือข่าย
ตระกูล	Machine learning	Process / pipeline
ปีกำเนิด	2001–2014	2002–2019 (algorithm family)
ผู้ริเริ่ม	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
ประเภท	Probabilistic generative model with Bayesian inference	Graph-partitioning / clustering algorithm family
แหล่งต้นตำรับ	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.
ชื่อเรียกอื่น	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
ที่เกี่ยวข้อง	5	5
สรุป	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.	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?