方法对比
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| 时间特征向量中心性× | 时间度中心性× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2011-2017 | 2011–2012 |
| 提出者≠ | Grindrod, P.; Higham, D. J.; Taylor, D. et al. | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. |
| 类型≠ | Centrality measure for temporal networks | Centrality measure (temporal extension) |
| 开创性文献≠ | Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120. DOI ↗ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| 别名 | dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC |
| 相关≠ | 5 | 6 |
| 摘要≠ | Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network. | Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window. |
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