Machine learningNetwork science
时间特征向量中心性
时间特征向量中心性将经典的特征向量中心性扩展到随时间变化的网络的分析中。通过考虑连接的顺序和时序，它识别出的节点不仅是因为拥有大量同时连接而具有影响力，更是因为它们处于网络多个时间切片中顺序重要路径的交叉点上。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120. DOI: 10.1103/PhysRevE.83.046120 ↗
- Taylor, D., Myers, S. A., Clauset, A., Porter, M. A., & Mucha, P. J. (2017). Eigenvector-based centrality measures for temporal networks. Multiscale Modeling and Simulation, 15(1), 537-574. DOI: 10.1137/16M1066142 ↗
如何引用本页
ScholarGate. (2026, June 3). Temporal Eigenvector Centrality (Dynamic Eigenvector-Based Node Importance in Time-Varying Networks). ScholarGate. https://scholargate.app/zh/network-analysis/temporal-eigenvector-centrality
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 特征向量中心性网络分析↔ compare
- 时间介数中心性网络分析↔ compare
- 时间度中心性网络分析↔ compare
- 时间PageRank网络分析↔ compare
- 时间社交网络分析网络分析↔ compare