Linganisha mbinu

	Uchambuzi wa Uenezaji wa Mitandao Mingi (Multilayer Network Diffusion Analysis)	Uchanganuzi wa Uenezaji wa Mtandao
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2013–2014	1927 (epidemic roots); network formalization 1990s–2000s
Mwanzilishi	Gomez, S. et al.; Boccaletti, S. et al.	Kermack, W. O. & McKendrick, A. G.
Aina	Network diffusion model	Simulation / analytical model
Chanzo asilia	Gomez, S., Diaz-Guilera, A., Gomez-Gardenes, J., Perez-Vicente, C. J., Moreno, Y., & Arenas, A. (2013). Diffusion dynamics on multiplex networks. Physical Review Letters, 110(2), 028701.	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.
Majina mbadala	multiplex diffusion analysis, multilayer spreading analysis, cross-layer contagion analysis, diffusion on multiplex networks	diffusion on networks, information diffusion, contagion spreading model, network propagation model
Zinazohusiana	6	5
Muhtasari	Multilayer Network Diffusion Analysis models how information, disease, or influence spreads across a system composed of multiple, interconnected network layers. By coupling diffusion processes across layers — for instance social ties, transport routes, and online channels simultaneously — it reveals how cross-layer interactions accelerate or dampen spreading and lowers epidemic thresholds compared to single-layer models.	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.