Wormhole: The Hidden Virus Propagation Power of a Search Engine in Social Networks - 2017


Today search engines are tightly let alone social networks, and present users with a double-edged sword: they're ready to amass information attention-grabbing to users but are capable of spreading viruses introduced by hackers. It is challenging to characterize how a research engine spreads viruses, since the search engine is a virtual virus pool and creates propagation methods over the underlying network structure. During this paper, we quantitatively analyze virus propagation effects and the stability of the virus propagation process in the presence of a search engine in social networks. Initial, although social networks have a community structure that impedes virus propagation, we find that a search engine generates a propagation wormhole. Second, we tend to propose an epidemic feedback model and quantitatively analyze propagation effects using four metrics: infection density, the propagation wormhole result, the epidemic threshold, and the basic replica range. Third, we verify our analyses on four real-world data sets and two simulated data sets. Moreover, we have a tendency to prove that the proposed model has the property of partial stability. Analysis results show that, compablack with to a case without a research engine present, virus propagation with the search engine features a higher infection density, shorter network diameter, greater propagation velocity, lower epidemic threshold, and bigger basic reproduction variety.

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