Before revision: The network paradigm is widely accepted as the gold standard in modelling complex systems such as epidemics or neuronal activity in the brain; however, in most cases, the exact nature of the network on which such dynamics unfold is unknown. This has motivated a significant amount of work on network inference. Whilst a large body of work is concerned with inferring network structure based on detailed node-level temporal data, in this work we tackle the more challenging scenario of inferring the family of the underlying network when only population-level temporal incidence data are available .
After revision: Using the continuous-time susceptible-infected-susceptible (SIS) model on networks, we investigate the problem of inferring the family of the underlying network when only population-level temporal incidence data are available .