Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika mērauklu īpašvērtību centrālās vērtības× | Īpašvektoru centralitāte× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2011-2017 | 1972 |
| Autors≠ | Grindrod, P.; Higham, D. J.; Taylor, D. et al. | Bonacich, P. |
| Tips≠ | Centrality measure for temporal networks | Centrality measure |
| Pirmavots≠ | Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120. DOI ↗ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Citi nosaukumi | dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network. | Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network. |
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