NDLI: Virus Propagation in Multiple Profile Networks

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Virus Propagation in Multiple Profile Networks

Content Provider	ACM Digital Library
Author	Tzimas, Giannis Sioutas, Spyros Tsichlas, Kostas Rapti, Angeliki
Abstract	Suppose we have a virus or one competing idea/product that propagates over a multiple profile (e.g., social) network. Can we predict what proportion of the network will actually get "infected" (e.g., spread the idea or buy the competing product), when the nodes of the network appear to have different sensitivity based on their profile? For example, if there are two profiles A and B in a network and the nodes of profile A and profile B are susceptible to a highly spreading virus with probabilities $β_{A}$ and $β_{B}$ respectively, what percentage of both profiles will actually get infected from the virus at the end? To reverse the question, what are the necessary conditions so that a predefined percentage of the network is infected? We assume that nodes of different profiles can infect one another and we prove that under realistic conditions, apart from the weak profile (great sensitivity), the stronger profile (low sensitivity) will get infected as well. First, we focus on cliques with the goal to provide exact theoretical results as well as to get some intuition as to how a virus affects such a multiple profile network. Then, we move to the theoretical analysis of arbitrary networks. We provide bounds on certain properties of the network based on the probabilities of infection of each node in it when it reaches the steady state. Finally, we provide extensive experimental results that verify our theoretical results and at the same time provide more insight on the problem.
Starting Page	975
Ending Page	984
Page Count	10
File Format	PDF MP4
ISBN	9781450336642
DOI	10.1145/2783258.2783386
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Epidemics Profiles Virus propagation
Content Type	Audio Text
Resource Type	Article

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