Сравнение методов

	Взвешенная центральность по близости	Центральность по близости
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2010	1950 (formalized 1979)
Автор метода	Opsahl, T.; Agneessens, F.; Skvoretz, J.	Bavelas, A.; formalized by Freeman, L. C.
Тип	Centrality measure (network analysis)	Node-level centrality index
Основополагающий источник	Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.	Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Другие названия	weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness	closeness, farness-based centrality, geodesic closeness, normalized closeness centrality
Связанные	6	6
Сводка	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.