Salīdzināt metodes

	Beieziešu multiplās tīklu analīze	Bayesiešu stohastiskais bloku modelis
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2014-2017	2001–2014
Autors	De Bacco, C. et al.; Kivela, M. et al.	Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.
Tips	Probabilistic generative model for multiplex networks	Probabilistic generative model with Bayesian inference
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.	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.
Citi nosaukumi	Bayesian multi-layer network analysis, probabilistic multiplex network inference, Bayesian multilayer network modelling, BMNA	Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model
Saistītās	4	5
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 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.