Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Centrálna miera časovej blízkosti× | Centralita blízosti× | |
|---|---|---|
| Odbor | Analýza sietí | Analýza sietí |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2011 | 1950 (formalized 1979) |
| Tvorca≠ | Pan, R. K. & Saramaki, J. | Bavelas, A.; formalized by Freeman, L. C. |
| Typ≠ | Centrality measure (temporal) | Node-level centrality index |
| Pôvodný zdroj≠ | 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 ↗ |
| Ďalšie názvy | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Príbuzné | 6 | 6 |
| Zhrnutie≠ | 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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