Identifying Sources of Random Walk-Based Epidemic Spreading in Networks

Abstract

Identifying the sources of epidemic spreading is of critical importance to epidemic control and network immunization. However, the task of source identification is very challenging, since in real situations the dynamics of the spreading process is usually not clear. In this paper, we formulate the multiple source epidemic spreading process as the multiple random walks, which is a theoretical model applicable to various spreading processes. Considering the different influence of distinct epidemic sources on the observed infection graph, we derive the maximum likelihood estimator of the multiple source identification problem. Simulation results on real-world networks and network models, such as the Price model and Erdos-Renyi (ER) model, demonstrate the efficiency of our estimator. Furthermore, we find that the efficiency of our estimator increases with the enhancement of network sparsity and heterogeneity.