Сравнение методов

	Динамическая центральность по близости	Динамическая центральность по степени
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2010–2012	2012
Автор метода	Tang, J. et al.; Holme, P. & Saramäki, J.	Holme, P. & Saramaki, J.; Kim, H. & Anderson, R.
Тип	Centrality measure for temporal networks	Centrality measure (temporal extension)
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.	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Другие названия	temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC	time-varying degree centrality, temporal degree centrality, evolving degree centrality, DDC
Связанные	5	5
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.	Dynamic degree centrality extends the classical degree centrality measure to networks that change over time. Rather than counting a node's connections in a single static snapshot, it tracks how many contacts each node maintains across successive time windows or contact events, producing a time-resolved importance profile for every actor in the network.