Effective immunization of online networks: a self-similar selection approach

This paper proposes a self-similar selection method as an alternative to existing immunization strategies for online networks. Given the self-similar characteristics of online networks which are shown to have fractal and scale-free structure, we presume that the self-similar selection which is well developed in physics outperforms random or targeted vaccination based on incoming or outgoing connections. We examine the effectiveness of the proposed self-similar selection method with random vaccination and other different types of targeted vaccination strategies in terms of delaying the spread of computer virus over a scale-free computer network constructed using real-world World Wide Web data. Our computer simulation results indicate that the self-similar selection method is more effective in deterring virus propagation than the existing vaccination strategies. In addition, vaccination based on self-similar selection is practical since it does not require detailed information about network morphology at the individual node level, which is often not easy to observe. Our findings have significant implications for both policy makers and network security providers.

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