Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Svērtais tuvuma centrālisms× | Svērtais īpašvērtību centralitātes rādītājs× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2010 | 1987 (binary); 2010 (weighted generalization) |
| Autors≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) |
| Tips≠ | Centrality measure (network analysis) | Spectral centrality measure |
| Pirmavots≠ | Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ |
| Citi nosaukumi | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart. | 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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