Võrdle meetodeid
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| [UNTRANSLATED]× | Vahel asuvus (Betweenness Centrality)× | |
|---|---|---|
| Valdkond | Võrgustikuanalüüs | Võrgustikuanalüüs |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2010s | 1977 |
| Looja≠ | Brandes, U. (betweenness); Bayesian extension developed by multiple authors (2010s) | Freeman, L. C. |
| Tüüp≠ | Probabilistic network centrality measure | Centrality measure |
| Algallikas≠ | Newman, M.E.J. (2010). Networks: An Introduction. Oxford University Press. ISBN: 978-0-19-920665-0 | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Rööpnimetused | Bayesian BC, probabilistic betweenness centrality, uncertainty-aware betweenness centrality, posterior betweenness estimation | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Seotud≠ | 3 | 6 |
| Kokkuvõte≠ | 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. | Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes. |
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