Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis de Difusión en Redes Temporales× | Análisis de Redes Multiplex× | |
|---|---|---|
| Campo | Análisis de redes | Análisis de redes |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2012 | 2014 |
| Autor original≠ | Holme, P. & Saramäki, J. | Kivela, M.; Boccaletti, S. et al. |
| Tipo≠ | Network analysis framework | Structural network model |
| Fuente seminal≠ | Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ |
| Alias | TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks | multiplex networks, multi-layer network analysis, multilayer network analysis, MNA |
| Relacionados≠ | 5 | 6 |
| 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. | Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities. |
| ScholarGateConjunto de datos ↗ |
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