Salīdzināt metodes

	Beieziešu multiplās tīklu analīze	Stohastiskais bloku modelis
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2014-2017	1983
Autors	De Bacco, C. et al.; Kivela, M. et al.	—
Tips	Probabilistic generative model for multiplex networks	Probabilistic generative graph model
Pirmavots	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.
Citi nosaukumi	Bayesian multi-layer network analysis, probabilistic multiplex network inference, Bayesian multilayer network modelling, BMNA	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Saistītās	4	7
Kopsavilkums	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.