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| تحليل الشبكات المتعددة الموزونة× | مركزية المتجه الذاتي الموزون× | |
|---|---|---|
| المجال | تحليل الشبكات | تحليل الشبكات |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 2014 | 1987 (binary); 2010 (weighted generalization) |
| صاحب الطريقة≠ | Battiston, F.; Kivela, M. et al. | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) |
| النوع≠ | Network analysis framework | Spectral centrality measure |
| المصدر التأسيسي≠ | Battiston, F., Nicosia, V., & Latora, V. (2014). Structural measures for multiplex networks. Physical Review E, 89(3), 032804. DOI ↗ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ |
| الأسماء البديلة | WMNA, weighted multilayer network analysis, weighted multi-relational network analysis, multiplex weighted graph analysis | WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige |
| 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 eigenvector centrality extends the classic eigenvector centrality measure to graphs where edges carry numerical weights, scoring each node proportionally to the sum of its neighbors' scores multiplied by the connecting edge weights. Nodes score highly not just by having many connections but by being strongly linked to other influential nodes, making the measure sensitive to both tie strength and network position simultaneously. |
| ScholarGateمجموعة البيانات ↗ |
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