Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Dinamiskā grādu centralitāte× | Svērtais pakāpes centralitāte× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2012 | 2004 |
| Autors≠ | Holme, P. & Saramaki, J.; Kim, H. & Anderson, R. | Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A. |
| Tips≠ | Centrality measure (temporal extension) | Centrality measure for weighted networks |
| Pirmavots≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. 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 ↗ |
| Citi nosaukumi | time-varying degree centrality, temporal degree centrality, evolving degree centrality, DDC | node strength, strength centrality, weighted node degree, WDC |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Dynamic degree centrality extends the classical degree centrality measure to networks that change over time. Rather than counting a node's connections in a single static snapshot, it tracks how many contacts each node maintains across successive time windows or contact events, producing a time-resolved importance profile for every actor in the network. | 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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