Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis de Difusión en Redes Temporales× | Análisis Temporal de Redes Sociales× | |
|---|---|---|
| Campo | Análisis de redes | Análisis de redes |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2012 | 2000s–2010s |
| Autor original≠ | Holme, P. & Saramäki, J. | Moody, J.; Holme, P.; Saramäki, J. |
| Tipo≠ | Network analysis framework | Longitudinal network analysis |
| Fuente seminal≠ | Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Alias | TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss. | Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time. |
| ScholarGateConjunto de datos ↗ |
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