Linganisha mbinu

	Mfumo wa Kuzuia Kazi nyingi wa Kimahesabu	Uchambuzi wa Uenezaji wa Mitandao Mingi (Multilayer Network Diffusion Analysis)
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2015-2017	2013–2014
Mwanzilishi	Peixoto, T. P.; De Bacco, C. and colleagues	Gomez, S. et al.; Boccaletti, S. et al.
Aina	Generative probabilistic model	Network diffusion model
Chanzo asilia	Peixoto, T. P. (2015). Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Physical Review E, 92(4), 042807.	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.
Majina mbadala	ML-SBM, multilayer SBM, multi-layer stochastic block model, multiplex stochastic block model	multiplex diffusion analysis, multilayer spreading analysis, cross-layer contagion analysis, diffusion on multiplex networks
Zinazohusiana	4	6
Muhtasari	The Multilayer Stochastic Block Model (ML-SBM) is a generative probabilistic framework that extends the classical stochastic block model to networks with multiple relation types or layers. It simultaneously infers community structure and block-to-block connection probabilities across all layers, capturing how communities cohere differently depending on context or relationship type.	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.