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| Độ trung tâm gần động× | Độ trung tâm giữa× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2010–2012 | 1977 |
| Người khởi xướng≠ | Tang, J. et al.; Holme, P. & Saramäki, J. | Freeman, L. C. |
| Loại≠ | Centrality measure for temporal networks | Centrality measure |
| Công trình gốc≠ | Tang, J., Musolesi, M., Mascolo, C., Latora, V. & Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. Proceedings of the 3rd Workshop on Social Network Systems (SNS '10). ACM. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Tên gọi khác | temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Liên quan≠ | 5 | 6 |
| Tóm tắt≠ | Dynamic closeness centrality extends classic closeness centrality to temporal networks by computing shortest time-respecting paths — paths that traverse edges in chronological order — and averaging inverse distances across all time windows. It reveals which nodes are most efficiently reached within an evolving network, tracking how a node's centrality rises and falls as connections appear and disappear over time. | 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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