Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika starpības centrālās vērtības noteikšana× | Vērstā starpniecības centralitāte× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2012 | 1977 |
| Autors≠ | Kim, H. & Anderson, R.; Holme, P. & Saramäki, J. | Freeman, L. C. |
| Tips≠ | Centrality measure for temporal networks | Centrality measure (directed graph) |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | 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. | Directed Betweenness Centrality extends Freeman's classic betweenness measure to directed graphs, quantifying how often a node lies on the shortest directed paths between all other pairs of nodes. It identifies gatekeepers, brokers, and bottlenecks in asymmetric flows such as information cascades, citation networks, and organizational hierarchies. |
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