Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika tuvuma centrālisms× | Tuvuma centralitāte× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2011 | 1950 (formalized 1979) |
| Autors≠ | Pan, R. K. & Saramaki, J. | Bavelas, A.; formalized by Freeman, L. C. |
| Tips≠ | Centrality measure (temporal) | Node-level centrality index |
| Pirmavots≠ | Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105. DOI ↗ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Citi nosaukumi | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Saistītās | 6 | 6 |
| 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. | Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts. |
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