方法对比
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| 动态紧密中心性× | 时态社群检测× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010–2012 | 2010 |
| 提出者≠ | Tang, J. et al.; Holme, P. & Saramäki, J. | Mucha, P. J. et al. |
| 类型≠ | Centrality measure for temporal networks | Network clustering algorithm |
| 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 ↗ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ |
| 别名 | temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| 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. | Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution. |
| ScholarGate数据集 ↗ |
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