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| 时间特征向量中心性× | 特征向量中心性× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2011-2017 | 1972 |
| 提出者≠ | Grindrod, P.; Higham, D. J.; Taylor, D. et al. | Bonacich, P. |
| 类型≠ | Centrality measure for temporal networks | Centrality measure |
| 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 ↗ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| 别名 | dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality | eigenvector centrality, EC, Bonacich centrality, power centrality |
| 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. | Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network. |
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