Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Directed Betweenness Centrality× | Центральность по направленной близости× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1977 | 1979–1994 |
| Автор метода≠ | Freeman, L. C. | Freeman, L. C.; Wasserman, S. & Faust, K. |
| Тип≠ | Centrality measure (directed graph) | Centrality measure |
| Основополагающий источник≠ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4 |
| Другие названия | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | Directed closeness centrality extends the classical closeness measure to directed networks by separately quantifying how quickly a node can be reached by others (in-closeness) and how quickly it can reach all others (out-closeness). It is a foundational node-level metric in social network analysis and graph theory, used wherever link direction conveys meaningful asymmetry such as citation flows, information cascades, or authority hierarchies. |
| ScholarGateНабор данных ↗ |
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