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| Độ trung tâm trung gian Bayes× | Phân tích mạng lưới xã hội Bayes× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2010s | 2002 |
| Người khởi xướng≠ | Brandes, U. (betweenness); Bayesian extension developed by multiple authors (2010s) | Hoff, P. D.; Raftery, A. E.; Handcock, M. S. |
| Loại≠ | Probabilistic network centrality measure | Probabilistic / Bayesian network model |
| Công trình gốc≠ | Newman, M.E.J. (2010). Networks: An Introduction. Oxford University Press. ISBN: 978-0-19-920665-0 | Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098. DOI ↗ |
| Tên gọi khác | Bayesian BC, probabilistic betweenness centrality, uncertainty-aware betweenness centrality, posterior betweenness estimation | Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling |
| Liên quan≠ | 3 | 5 |
| Tóm tắt≠ | Bayesian Betweenness Centrality estimates how often a node lies on shortest paths in a network while explicitly quantifying uncertainty arising from incomplete, sampled, or noisy edge observations. Rather than producing a single point estimate, it yields a posterior distribution over betweenness scores, enabling credible intervals and probabilistic comparisons between nodes. | Bayesian Social Network Analysis applies Bayesian probabilistic inference to relational data, placing prior distributions over network parameters and updating them with observed tie data to yield full posterior distributions over structural features, tie probabilities, and latent actor positions. It enables principled uncertainty quantification in network models, making it especially valuable when data are sparse, partially observed, or subject to measurement error. |
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