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| 加权模块度分析× | 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≠ | 2004 | 2010 |
| 提出者≠ | Newman, M. E. J. | Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001) |
| 类型≠ | Community structure optimization on weighted graphs | Centrality measure (path-based) |
| 开创性文献≠ | Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. 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 modularity, weighted Q optimization, weighted network community detection, strength-based modularity | WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted) |
| 相关≠ | 5 | 6 |
| 摘要≠ | Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully. | 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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