Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ мультиплексных сетей× | Анализ сетевой диффузии× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2014 | 1927 (epidemic roots); network formalization 1990s–2000s |
| Автор метода≠ | Kivela, M.; Boccaletti, S. et al. | Kermack, W. O. & McKendrick, A. G. |
| Тип≠ | Structural network model | Simulation / analytical model |
| Основополагающий источник≠ | 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 ↗ | Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721. DOI ↗ |
| Другие названия | multiplex networks, multi-layer network analysis, multilayer network analysis, MNA | diffusion on networks, information diffusion, contagion spreading model, network propagation model |
| Связанные≠ | 6 | 5 |
| Сводка≠ | 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. | Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally. |
| ScholarGateНабор данных ↗ |
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