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| Centralność stopnia czasowego× | Centralność stopnia× | |
|---|---|---|
| Dziedzina | Analiza sieci | Analiza sieci |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2011–2012 | 1978 |
| Twórca≠ | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. | Freeman, L. C. |
| Typ≠ | Centrality measure (temporal extension) | Node-level centrality measure |
| Źródło pierwotne≠ | 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 ↗ |
| Inne nazwy | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC | node degree, degree score, DC, connectivity centrality |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window. | 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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