Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Зважений аналіз часових мереж× | Аналіз поширення в мережі× | |
|---|---|---|
| Галузь | Мережевий аналіз | Мережевий аналіз |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2004–2012 | 1927 (epidemic roots); network formalization 1990s–2000s |
| Автор методу≠ | Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks) | Kermack, W. O. & McKendrick, A. G. |
| Тип≠ | Network analysis technique | Simulation / analytical model |
| Основоположне джерело≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. 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 ↗ |
| Інші назви | WTNA, weighted time-varying network analysis, weighted dynamic network analysis, weighted evolving network analysis | diffusion on networks, information diffusion, contagion spreading model, network propagation model |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | Weighted temporal network analysis studies networks whose edges carry numerical weights — representing interaction strength, frequency, or intensity — and whose structure changes over time. It combines the time-varying perspective of temporal network analysis with the quantitative precision of weighted graph metrics, revealing not only when connections exist but how strong they are at each moment. | 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. |
| ScholarGateНабір даних ↗ |
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