Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Bayesovská centrálnost podle průchodnosti× | Vážená středovost mezi uzly× | |
|---|---|---|
| Obor | Analýza sítí | Analýza sítí |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2010s | 2010 |
| Tvůrce≠ | Brandes, U. (betweenness); Bayesian extension developed by multiple authors (2010s) | Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001) |
| Typ≠ | Probabilistic network centrality measure | Centrality measure (path-based) |
| Původní zdroj≠ | 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. DOI ↗ |
| Další názvy | Bayesian BC, probabilistic betweenness centrality, uncertainty-aware betweenness centrality, posterior betweenness estimation | WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted) |
| Příbuzné≠ | 3 | 6 |
| Shrnutí≠ | 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. |
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