Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Зважена центральність за близькістю× | Центральність за близькістю× | |
|---|---|---|
| Галузь | Мережевий аналіз | Мережевий аналіз |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2010 | 1950 (formalized 1979) |
| Автор методу≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Bavelas, A.; formalized by Freeman, L. C. |
| Тип≠ | Centrality measure (network analysis) | Node-level centrality index |
| Основоположне джерело≠ | Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Інші назви | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Пов'язані | 6 | 6 |
| Підсумок≠ | Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart. | 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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