Сравнение на методи

	Динамична централност по близост	Времеви анализ на социални мрежи
Област	Мрежови анализ	Мрежови анализ
Семейство	Machine learning	Machine learning
Година на възникване	2010–2012	2000s–2010s
Създател	Tang, J. et al.; Holme, P. & Saramäki, J.	Moody, J.; Holme, P.; Saramäki, J.
Тип	Centrality measure for temporal networks	Longitudinal network analysis
Основополагащ източник	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.
Други названия	temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA
Свързани	5	4
Резюме	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.