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| Dynamische Closeness-Zentralität× | Betweenness-Zentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2010–2012 | 1977 |
| Urheber≠ | Tang, J. et al.; Holme, P. & Saramäki, J. | Freeman, L. C. |
| Typ≠ | Centrality measure for temporal networks | Centrality measure |
| 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. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Aliasnamen | temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| 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. | Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes. |
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