Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Časová vlastní vektorová centralita× | Časová analýza sociálních sítí× | |
|---|---|---|
| Obor | Analýza sítí | Analýza sítí |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2011-2017 | 2000s–2010s |
| Tvůrce≠ | Grindrod, P.; Higham, D. J.; Taylor, D. et al. | Moody, J.; Holme, P.; Saramäki, J. |
| Typ≠ | Centrality measure for temporal networks | Longitudinal network analysis |
| Původní zdroj≠ | Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120. DOI ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Další názvy | dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| Příbuzné≠ | 5 | 4 |
| Shrnutí≠ | 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. | Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time. |
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