Methoden vergleichen

	Dynamische Closeness-Zentralität	Klupplungzentralität
Fachgebiet	Netzwerkanalyse	Netzwerkanalyse
Familie	Machine learning	Machine learning
Entstehungsjahr	2010–2012	1950 (formalized 1979)
Urheber	Tang, J. et al.; Holme, P. & Saramäki, J.	Bavelas, A.; formalized by Freeman, L. C.
Typ	Centrality measure for temporal networks	Node-level centrality index
Wegweisende Quelle	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. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Aliasnamen	temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC	closeness, farness-based centrality, geodesic closeness, normalized closeness centrality
Verwandt	5	6
Zusammenfassung	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.	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.