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| Centralità di Closeness Dinamica× | Centralità del grado dinamica× | |
|---|---|---|
| Campo | Analisi delle reti | Analisi delle reti |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2010–2012 | 2012 |
| Ideatore≠ | Tang, J. et al.; Holme, P. & Saramäki, J. | Holme, P. & Saramaki, J.; Kim, H. & Anderson, R. |
| Tipo≠ | Centrality measure for temporal networks | Centrality measure (temporal extension) |
| Fonte seminale≠ | 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 ↗ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Alias | temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC | time-varying degree centrality, temporal degree centrality, evolving degree centrality, DDC |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. | Dynamic degree centrality extends the classical degree centrality measure to networks that change over time. Rather than counting a node's connections in a single static snapshot, it tracks how many contacts each node maintains across successive time windows or contact events, producing a time-resolved importance profile for every actor in the network. |
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