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| Centralność pośrednictwa czasowego× | Centralność pośrednictwa× | |
|---|---|---|
| Dziedzina | Analiza sieci | Analiza sieci |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2012 | 1977 |
| Twórca≠ | Kim, H. & Anderson, R.; Holme, P. & Saramäki, J. | Freeman, L. C. |
| Typ≠ | Centrality measure for temporal networks | Centrality measure |
| Źródło pierwotne≠ | 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 ↗ |
| Inne nazwy | TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | 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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