방법 비교

	베이지안 다중망 분석	확률적 블록 모형 (Stochastic Block Model, SBM)
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Process / pipeline
기원 연도	2014-2017	1983
창시자	De Bacco, C. et al.; Kivela, M. et al.	—
유형	Probabilistic generative model for multiplex networks	Probabilistic generative graph model
원전	De Bacco, C., Power, E. A., Larremore, D. B., & Moore, C. (2017). Community detection, link prediction, and layer interdependence in multilayer networks. Physical Review E, 95(4), 042317.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
별칭	Bayesian multi-layer network analysis, probabilistic multiplex network inference, Bayesian multilayer network modelling, BMNA	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
관련	4	7
요약	Bayesian multiplex network analysis applies probabilistic generative modelling to networks that carry more than one type of relational tie simultaneously — such as friendship, collaboration, and communication links among the same set of actors. By placing priors over community memberships, edge probabilities, and layer interdependencies, the framework yields posterior distributions rather than point estimates, supporting principled uncertainty quantification across all inferred network properties.	The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.