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| 加权紧密度中心性× | 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 | 2010 | 2010 |
| 提出者≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001) |
| 类型≠ | Centrality measure (network analysis) | Centrality measure (path-based) |
| 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 ↗ | 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 | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted) |
| 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 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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