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| Байесов мултиплексен мрежов анализ× | Бейсовско откриване на общности× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2014-2017 | 2001–2014 |
| Създател≠ | De Bacco, C. et al.; Kivela, M. et al. | Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P. |
| Тип≠ | Probabilistic generative model for multiplex networks | Probabilistic generative model / inference |
| 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. DOI ↗ | Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804. DOI ↗ |
| Други названия | 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 |
| Свързани≠ | 4 | 5 |
| 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. | 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. |
| ScholarGateНабор от данни ↗ |
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