Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Temporal Betweenness Centrality× | Directed Betweenness Centrality× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2012 | 1977 |
| Автор метода≠ | Kim, H. & Anderson, R.; Holme, P. & Saramäki, J. | Freeman, L. C. |
| Тип≠ | Centrality measure for temporal networks | Centrality measure (directed graph) |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness |
| Связанные≠ | 6 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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