Machine learningNetwork science
时间度中心性
时间度中心性将经典的度中心性扩展到时变网络，通过计算节点在一段时间内累积的不同联系数量来衡量。它不是将动态网络折叠成一个静态图，而是保留了边的时序顺序，从而更真实地衡量节点在观测窗口内的活跃度和可达性。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI: 10.1016/j.physrep.2012.03.001 ↗
- Kim, H. & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E, 85(2), 026107. DOI: 10.1103/PhysRevE.85.026107 ↗
如何引用本页
ScholarGate. (2026, June 3). Temporal Degree Centrality in Time-Varying Networks. ScholarGate. https://scholargate.app/zh/network-analysis/temporal-degree-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.
- 度中心性网络分析↔ compare
- 时间介数中心性网络分析↔ compare
- 时间邻近中心性网络分析↔ compare
- 时间特征向量中心性网络分析↔ compare
- 时间PageRank网络分析↔ compare
- 时间社交网络分析网络分析↔ compare