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ベイジアン多重ネットワーク分析×多重ネットワーク分析×
分野ネットワーク分析ネットワーク分析
系統Machine learningMachine learning
提唱年2014-20172014
提唱者De Bacco, C. et al.; Kivela, M. et al.Kivela, M.; Boccaletti, S. et al.
種類Probabilistic generative model for multiplex networksStructural network model
原典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. DOI ↗Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗
別名Bayesian multi-layer network analysis, probabilistic multiplex network inference, Bayesian multilayer network modelling, BMNAmultiplex networks, multi-layer network analysis, multilayer network analysis, MNA
関連46
概要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.Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities.
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ScholarGate手法を比較: Bayesian Multiplex Network Analysis · Multiplex Network Analysis. 2026-06-15に以下より取得 https://scholargate.app/ja/compare