Methoden vergleichen

	Dynamische Closeness-Zentralität	Zeitliche Gemeinschaftserkennung
Fachgebiet	Netzwerkanalyse	Netzwerkanalyse
Familie	Machine learning	Machine learning
Entstehungsjahr	2010–2012	2010
Urheber	Tang, J. et al.; Holme, P. & Saramäki, J.	Mucha, P. J. et al.
Typ	Centrality measure for temporal networks	Network clustering algorithm
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.	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.
Aliasnamen	temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
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.	Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.