方法对比

	贝叶斯多层网络分析	贝叶斯社群侦测
领域	网络分析	网络分析
方法族	Machine learning	Machine learning
起源年份	2014-2017	2001–2014
提出者	De Bacco, C. et al.; Kivela, M. et al.	Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.
类型	Probabilistic generative model for multiplex networks	Probabilistic generative model / inference
开创性文献	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.
别名	Bayesian multi-layer network analysis, probabilistic multiplex network inference, Bayesian multilayer network modelling, BMNA	Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning
相关	4	5
摘要	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.	Bayesian community detection infers latent group structure in networks by treating community membership as unobserved variables and using Bayesian inference — typically via Markov chain Monte Carlo or variational methods — to compute a posterior distribution over all plausible partitions. Unlike modularity optimisation, it selects the number of communities from data and provides principled uncertainty estimates for every node assignment.