قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| مركزية البينية الموجهة× | مركزية القرب الموجه× | |
|---|---|---|
| المجال | تحليل الشبكات | تحليل الشبكات |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 1977 | 1979–1994 |
| صاحب الطريقة≠ | Freeman, L. C. | Freeman, L. C.; Wasserman, S. & Faust, K. |
| النوع≠ | Centrality measure (directed graph) | Centrality measure |
| المصدر التأسيسي≠ | 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 |
| الأسماء البديلة | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness |
| ذات صلة | 5 | 5 |
| الملخص≠ | 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. |
| ScholarGateمجموعة البيانات ↗ |
|
|