Salīdzināt metodes

	Beieziešu centrālās vērtības (Bayesian Betweenness Centrality)	Svērtais starpniecības centrālums
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2010s	2010
Autors	Brandes, U. (betweenness); Bayesian extension developed by multiple authors (2010s)	Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)
Tips	Probabilistic network centrality measure	Centrality measure (path-based)
Pirmavots	Newman, M.E.J. (2010). Networks: An Introduction. Oxford University Press. ISBN: 978-0-19-920665-0	Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.
Citi nosaukumi	Bayesian BC, probabilistic betweenness centrality, uncertainty-aware betweenness centrality, posterior betweenness estimation	WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted)
Saistītās	3	6
Kopsavilkums	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.	Weighted Betweenness Centrality extends Freeman's betweenness measure to edge-weighted graphs by routing shortest paths through a tunable transformation of edge weights. Nodes that sit on many high-value shortest paths receive high scores, identifying brokers and bridges in social, biological, and information networks where tie strength matters.