Linganisha mbinu

	Uchambuzi wa Uenezaji wa Mitandao Mingi (Multilayer Network Diffusion Analysis)	Uchambuzi wa Usambaaji wa Mitandao ya Muda
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2013–2014	2012
Mwanzilishi	Gomez, S. et al.; Boccaletti, S. et al.	Holme, P. & Saramäki, J.
Aina	Network diffusion model	Network analysis framework
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.	Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Majina mbadala	multiplex diffusion analysis, multilayer spreading analysis, cross-layer contagion analysis, diffusion on multiplex networks	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks
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.	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.