In conclusions, we have presented in this work a proof-ofconcept study of a novel epidemic mitigation method based on equal graph partitioning of the estimated infection network. We used computer simulations to study and show the effectiveness of the method. First, we followed the intuitive JGN method to generate artificial social network of various sizes but with roughly the same clustering characteristic to serve as the infection networks. We then simulated SIR epidemics on the artificial infection networks, recorded the incidences, and applied censorship with rates between 50% and 85% to mimic low reporting rates for mild infections. We then used the remaining incidence data to construct an estimate of the infection network using a modified JGN algorithm. We found that the estimation accuracy falls with increasing censor rate, average degree, and size of the network, but improves as more data from multiple epidemics are incorporated.