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 almost 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 designing and deploying a digital immune system.
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Strictly speaking, our algorithm is neither completely local nor global. In fact, by tuning the distance d of the immunization (covering) strategy one can move from a truly local algorithm to an algorithm close to the targeted immunization approach for d∼D, being D the diameter of the network. In this sense, our method is half-a-way between strictly local and global strategies. This difference diffuses when one consider ultra-small world networks, which is not our case
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Gómez-Gardeñes, J., Echenique, P. & Moreno, Y. Immunization of real complex communication networks. Eur. Phys. J. B 49, 259–264 (2006). https://doi.org/10.1140/epjb/e2006-00041-1
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DOI: https://doi.org/10.1140/epjb/e2006-00041-1