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| Времева централност по близост× | Централност по близост× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2011 | 1950 (formalized 1979) |
| Създател≠ | Pan, R. K. & Saramaki, J. | Bavelas, A.; formalized by Freeman, L. C. |
| Тип≠ | Centrality measure (temporal) | Node-level centrality index |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Свързани | 6 | 6 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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