Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Centralité d'intermédiarité temporelle× | Centralité d'intermédiarité× | |
|---|---|---|
| Domaine | Analyse de réseaux | Analyse de réseaux |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2012 | 1977 |
| Auteur d'origine≠ | Kim, H. & Anderson, R.; Holme, P. & Saramäki, J. | Freeman, L. C. |
| Type≠ | Centrality measure for temporal networks | Centrality measure |
| Source fondatrice≠ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Alias | TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Apparentées | 6 | 6 |
| Résumé≠ | Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot. | 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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