Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laikmeta pakāpes centralitāte× | Laika tuvuma centrālisms× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2011–2012 | 2011 |
| Autors≠ | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. | Pan, R. K. & Saramaki, J. |
| Tips≠ | Centrality measure (temporal extension) | Centrality measure (temporal) |
| Pirmavots≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105. DOI ↗ |
| Citi nosaukumi | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality |
| Saistītās | 6 | 6 |
| 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 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. |
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