Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Центральність за часовою близькістю× | Центральність за близькістю× | |
|---|---|---|
| Галузь | Мережевий аналіз | Мережевий аналіз |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2011 | 1950 (formalized 1979) |
| Автор методу≠ | Pan, R. K. & Saramaki, J. | Bavelas, A.; formalized by Freeman, L. C. |
| Тип≠ | Centrality measure (temporal) | Node-level centrality index |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Пов'язані | 6 | 6 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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