Machine learningNetwork science
贝叶斯介数中心性
贝叶斯介数中心性（Bayesian Betweenness Centrality）估算一个节点在网络最短路径上出现的频率，同时明确量化由不完整、抽样或有噪声的边观测所带来的不确定性。它不产生单一的点估计值，而是生成介数得分的后验分布，从而能够计算可信区间和节点间的概率比较。
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来源
- Newman, M.E.J. (2010). Networks: An Introduction. Oxford University Press. ISBN: 978-0-19-920665-0
- Fortunato, S., Bergstrom, C.T., Borner, K., Evans, J.A., Helbing, D., Milojevi, S., Petersen, A.M., Radicchi, F., Sinatra, R., Uzzi, B., Vespignani, A., Waltman, L., Wang, D. & Barabasi, A.-L. (2018). Science of science. Science, 359(6379), eaao0185. DOI: 10.1126/science.aao0185 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Betweenness Centrality (Probabilistic Inference of Shortest-Path Centrality). ScholarGate. https://scholargate.app/zh/network-analysis/bayesian-betweenness-centrality
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