Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelos de difusión en redes× | Análisis de Redes Temporales× | |
|---|---|---|
| Campo | Análisis de redes | Análisis de redes |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1927 (epidemiological compartmental); 2003 (social influence cascade) | 2012 |
| Autor original≠ | Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003) | Holme & Saramäki (2012) — seminal framework |
| Tipo≠ | Stochastic / deterministic simulation on graphs | Dynamic graph analysis |
| Fuente seminal≠ | Kermack, W.O. & McKendrick, A.G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, 115(772), 700-721. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ |
| Alias≠ | epidemic spreading models, compartmental models, influence propagation models, Ağ Yayılım Modelleri (SIR, SIS, Independent Cascade) | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| Relacionados≠ | 5 | 3 |
| Resumen≠ | Network diffusion models are a family of compartmental and probabilistic frameworks that simulate how information, disease, or innovation spreads across a connected system. Rooted in the mathematical epidemiology of Kermack and McKendrick (1927), the SIR and SIS models partition nodes into states and track transitions driven by contact rates and recovery probabilities. The Independent Cascade and Linear Threshold models, formalised by Kempe, Kleinberg, and Tardos (2003), extend this logic to social influence, modelling how activation propagates through a network one neighbour at a time. | Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system. |
| ScholarGateConjunto de datos ↗ |
|
|