Deep Demixing: Reconstructing the Evolution of Epidemics using Graph Neural Networks
Deep Demixing: Reconstructing the Evolution of Epidemics using Graph Neural Networks
We study the temporal reconstruction of epidemics evolving over networks. Given partial or aggregated temporal information of the epidemic, our goal is to estimate the complete evolution of the spread leveraging the topology of the network but being agnostic to the precise epidemic model. We overcome this lack of model …