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| 加权特征向量中心性× | 加权介数中心性× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1987 (binary); 2010 (weighted generalization) | 2010 |
| 提出者≠ | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) | Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001) |
| 类型≠ | Spectral centrality measure | Centrality measure (path-based) |
| 开创性文献≠ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ | Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ |
| 别名 | WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige | WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted) |
| 相关 | 6 | 6 |
| 摘要≠ | Weighted eigenvector centrality extends the classic eigenvector centrality measure to graphs where edges carry numerical weights, scoring each node proportionally to the sum of its neighbors' scores multiplied by the connecting edge weights. Nodes score highly not just by having many connections but by being strongly linked to other influential nodes, making the measure sensitive to both tie strength and network position simultaneously. | Weighted Betweenness Centrality extends Freeman's betweenness measure to edge-weighted graphs by routing shortest paths through a tunable transformation of edge weights. Nodes that sit on many high-value shortest paths receive high scores, identifying brokers and bridges in social, biological, and information networks where tie strength matters. |
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