方法对比
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| 动态紧密中心性× | 接近中心性× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010–2012 | 1950 (formalized 1979) |
| 提出者≠ | Tang, J. et al.; Holme, P. & Saramäki, J. | Bavelas, A.; formalized by Freeman, L. C. |
| 类型≠ | Centrality measure for temporal networks | Node-level centrality index |
| 开创性文献≠ | 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. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| 别名 | temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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. | Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts. |
| ScholarGate数据集 ↗ |
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