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| 加权紧密度中心性× | 加权度中心性× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010 | 2004 |
| 提出者≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A. |
| 类型≠ | Centrality measure (network analysis) | Centrality measure for weighted networks |
| 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 ↗ | Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ |
| 别名 | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | node strength, strength centrality, weighted node degree, WDC |
| 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 degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score. |
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