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| Dynamische Gradzentralität× | Gradzentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2012 | 1978 |
| Urheber≠ | Holme, P. & Saramaki, J.; Kim, H. & Anderson, R. | Freeman, L. C. |
| Typ≠ | Centrality measure (temporal extension) | Node-level centrality measure |
| Wegweisende Quelle≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Aliasnamen | time-varying degree centrality, temporal degree centrality, evolving degree centrality, DDC | node degree, degree score, DC, connectivity centrality |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | 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. | Degree centrality is the simplest and most intuitive measure of a node's importance in a network, defined as the number of direct ties a node has to other nodes. Normalized by dividing by the maximum possible ties, it allows comparison across networks of different sizes and is the starting point of almost every network analysis. |
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