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| Temporale Eigenvektorzentralität× | Zeitliche Analyse sozialer Netzwerke× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2011-2017 | 2000s–2010s |
| Urheber≠ | Grindrod, P.; Higham, D. J.; Taylor, D. et al. | Moody, J.; Holme, P.; Saramäki, J. |
| Typ≠ | Centrality measure for temporal networks | Longitudinal network analysis |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen | dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | 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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