Сравнение методов

	Динамическая центральность по близости	Центральность по посредничеству
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2010–2012	1977
Автор метода	Tang, J. et al.; Holme, P. & Saramäki, J.	Freeman, L. C.
Тип	Centrality measure for temporal networks	Centrality measure
Основополагающий источник	Tang, J., Musolesi, M., Mascolo, C., Latora, V. & Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. Proceedings of the 3rd Workshop on Social Network Systems (SNS '10). ACM.	Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.
Другие названия	temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC	Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness
Связанные	5	6
Сводка	Dynamic closeness centrality extends classic closeness centrality to temporal networks by computing shortest time-respecting paths — paths that traverse edges in chronological order — and averaging inverse distances across all time windows. It reveals which nodes are most efficiently reached within an evolving network, tracking how a node's centrality rises and falls as connections appear and disappear over time.	Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes.