Linganisha mbinu

	Uthabiti wa Kati kati ya Bayesian (Bayesian Betweenness Centrality)	Uchanganuzi wa Mitandao ya Kijamii wa Kibayesiani
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2010s	2002
Mwanzilishi	Brandes, U. (betweenness); Bayesian extension developed by multiple authors (2010s)	Hoff, P. D.; Raftery, A. E.; Handcock, M. S.
Aina	Probabilistic network centrality measure	Probabilistic / Bayesian network model
Chanzo asilia	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.
Majina mbadala	Bayesian BC, probabilistic betweenness centrality, uncertainty-aware betweenness centrality, posterior betweenness estimation	Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling
Zinazohusiana	3	5
Muhtasari	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.