Machine learningNetwork science
加权特征向量中心性
加权特征向量中心性将经典的特征向量中心性度量扩展到具有数值权重边的图，其评分方式是每个节点的得分与其邻居得分的总和乘以连接边的权重成正比。节点得分高不仅是因为拥有许多连接，更是因为与许多有影响力的节点紧密相连，这使得该度量能够同时对连接强度和网络位置做出反应。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI: 10.1086/228631 ↗
- Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI: 10.1016/j.socnet.2010.03.006 ↗
如何引用本页
ScholarGate. (2026, June 3). Weighted Eigenvector Centrality (Spectral Prestige in Weighted Networks). ScholarGate. https://scholargate.app/zh/network-analysis/weighted-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.
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- 加权度中心性网络分析↔ compare
- 加权PageRank网络分析↔ compare