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× | 加权社会网络分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010 | 2004–2010 |
| 提出者≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Barrat, A.; Opsahl, T. et al. |
| 类型≠ | Centrality measure (network analysis) | Network analysis framework |
| 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 | Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis |
| 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 Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships. |
| ScholarGate数据集 ↗ |
|
|