Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Vážená centralita vlastního vektoru× | Vážená decentrální míra stupně× | |
|---|---|---|
| Obor | Analýza sítí | Analýza sítí |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1987 (binary); 2010 (weighted generalization) | 2004 |
| Tvůrce≠ | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) | Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A. |
| Typ≠ | Spectral centrality measure | Centrality measure for weighted networks |
| Původní zdroj≠ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ | Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ |
| Další názvy | WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige | node strength, strength centrality, weighted node degree, WDC |
| Příbuzné | 6 | 6 |
| Shrnutí≠ | 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. | Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score. |
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