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| تحليل الشبكات المتعددة الموزونة× | مركزية الوساطة الموزونة× | |
|---|---|---|
| المجال | تحليل الشبكات | تحليل الشبكات |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 2014 | 2010 |
| صاحب الطريقة≠ | Battiston, F.; Kivela, M. et al. | Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001) |
| النوع≠ | Network analysis framework | Centrality measure (path-based) |
| المصدر التأسيسي≠ | Battiston, F., Nicosia, V., & Latora, V. (2014). Structural measures for multiplex networks. Physical Review E, 89(3), 032804. 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 ↗ |
| الأسماء البديلة | WMNA, weighted multilayer network analysis, weighted multi-relational network analysis, multiplex weighted graph analysis | WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted) |
| ذات صلة≠ | 5 | 6 |
| الملخص≠ | Weighted multiplex network analysis studies systems in which the same set of actors are connected through multiple types of relationships simultaneously, and each relationship carries a quantitative strength or frequency. By capturing both the variety and the intensity of ties across layers, it reveals patterns invisible to single-layer or unweighted network approaches. | 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. |
| ScholarGateمجموعة البيانات ↗ |
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