Immunization of Real Complex Communication Networks

Abstract

Most communication networks are complex. In this paper, we address one of the fundamental problems we are facing nowadays, namely, how we can efficiently protect these networks. To this end, we study an immunization strategy and found that it works as good as targeted immunization, but using only local information about the network topology. Our findings are supported with numerical simulations of the Susceptible-Infected-Removed (SIR) model on top of real communication networks, where immune nodes are previously identified by a covering algorithm. The results provide useful hints in the way to design and deploying a digital immune system.