Сравнение на методи

	Многослоен стохастичен блокови модел	Анализ на дифузия в многоетажни мрежи
Област	Мрежови анализ	Мрежови анализ
Семейство	Machine learning	Machine learning
Година на възникване	2015-2017	2013–2014
Създател	Peixoto, T. P.; De Bacco, C. and colleagues	Gomez, S. et al.; Boccaletti, S. et al.
Тип	Generative probabilistic model	Network diffusion model
Основополагащ източник	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.
Други названия	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
Свързани	4	6
Резюме	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.