Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika tuvuma centrālisms× | Temporālā sociālo tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2011 | 2000s–2010s |
| Autors≠ | Pan, R. K. & Saramaki, J. | Moody, J.; Holme, P.; Saramäki, J. |
| Tips≠ | Centrality measure (temporal) | Longitudinal network analysis |
| Pirmavots≠ | Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105. DOI ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Citi nosaukumi | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | Temporal closeness centrality extends the classical closeness measure to time-varying networks by replacing static shortest paths with time-respecting (foremost) paths. It quantifies how quickly a node can reach all other nodes when interactions occur at specific moments in time, giving a more realistic picture of information flow, disease spread, and influence in dynamic systems. | 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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