Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Centralidad de Grado Temporal× | Centralidad de Cercanía Temporal× | |
|---|---|---|
| Campo | Análisis de redes | Análisis de redes |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2011–2012 | 2011 |
| Autor original≠ | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. | Pan, R. K. & Saramaki, J. |
| Tipo≠ | Centrality measure (temporal extension) | Centrality measure (temporal) |
| Fuente seminal≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105. DOI ↗ |
| Alias | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based 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. | Temporal closeness centrality extends the classical closeness measure to time-varying networks by replacing static shortest paths with time-respecting (foremost) paths. It quantifies how quickly a node can reach all other nodes when interactions occur at specific moments in time, giving a more realistic picture of information flow, disease spread, and influence in dynamic systems. |
| ScholarGateConjunto de datos ↗ |
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