Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Svērta multipleksu tīklu analīze× | Svērtais īpašvērtību centralitātes rādītājs× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2014 | 1987 (binary); 2010 (weighted generalization) |
| Autors≠ | Battiston, F.; Kivela, M. et al. | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) |
| Tips≠ | Network analysis framework | Spectral centrality measure |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | 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 |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | 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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