Сравнение на методи

	Байесов мултиплексен мрежов анализ	Стохастичен блокови модел
Област	Мрежови анализ	Мрежови анализ
Семейство	Machine learning	Process / pipeline
Година на възникване	2014-2017	1983
Създател	De Bacco, C. et al.; Kivela, M. et al.	—
Тип	Probabilistic generative model for multiplex networks	Probabilistic generative graph model
Основополагащ източник	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.
Други названия	Bayesian multi-layer network analysis, probabilistic multiplex network inference, Bayesian multilayer network modelling, BMNA	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Свързани	4	7
Резюме	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.