Сравнение методов

	Динамическая центральность по близости	Временной анализ социальных сетей
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2010–2012	2000s–2010s
Автор метода	Tang, J. et al.; Holme, P. & Saramäki, J.	Moody, J.; Holme, P.; Saramäki, J.
Тип	Centrality measure for temporal networks	Longitudinal network analysis
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., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Другие названия	temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA
Связанные	5	4
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.	Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time.