Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laikmeta pakāpes centralitāte× | Temporālā sociālo tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2011–2012 | 2000s–2010s |
| Autors≠ | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. | Moody, J.; Holme, P.; Saramäki, J. |
| Tips≠ | Centrality measure (temporal extension) | Longitudinal network analysis |
| Pirmavots≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Citi nosaukumi | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window. | 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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