Before revision:  This result provides an a priori characterisation of different network families. Then, given populationlevel temporal epidemic data, we employ a Bayesian classifier to derive posterior distributions over diferent network families .
After revision:  We then use Bayesian model selection to recover the most likely underlying network class, based only on a single realisation of the epidemic .