Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Centralidad de Grado Temporal× | Centralidad de grado× | |
|---|---|---|
| Campo | Análisis de redes | Análisis de redes |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2011–2012 | 1978 |
| Autor original≠ | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. | Freeman, L. C. |
| Tipo≠ | Centrality measure (temporal extension) | Node-level centrality measure |
| Fuente seminal≠ | 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 ↗ |
| Alias | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC | node degree, degree score, DC, connectivity centrality |
| Relacionados | 6 | 6 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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