Сравнение методов

	Взвешенная степень центральности	Центральность по близости
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2004	1950 (formalized 1979)
Автор метода	Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.	Bavelas, A.; formalized by Freeman, L. C.
Тип	Centrality measure for weighted networks	Node-level centrality index
Основополагающий источник	Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.	Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Другие названия	node strength, strength centrality, weighted node degree, WDC	closeness, farness-based centrality, geodesic closeness, normalized closeness centrality
Связанные	6	6
Сводка	Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score.	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.