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| Phân tích mạng đa tầng có trọng số× | Trọng số Trung tâm Vector riêng× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2014 | 1987 (binary); 2010 (weighted generalization) |
| Người khởi xướng≠ | Battiston, F.; Kivela, M. et al. | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) |
| Loại≠ | Network analysis framework | Spectral centrality measure |
| Công trình gốc≠ | 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 ↗ |
| Tên gọi khác | 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 |
| Liên quan≠ | 5 | 6 |
| Tóm tắt≠ | 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. |
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