方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 加权紧密度中心性× | 加权特征向量中心性× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010 | 1987 (binary); 2010 (weighted generalization) |
| 提出者≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) |
| 类型≠ | Centrality measure (network analysis) | Spectral centrality measure |
| 开创性文献≠ | Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ |
| 别名 | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige |
| 相关 | 6 | 6 |
| 摘要≠ | Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart. | 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. |
| ScholarGate数据集 ↗ |
|
|