14,866 research outputs found
Computer Virus Propagation in a Network Organization: The Interplay between Social and Technological Networks
This paper proposes a holistic view of a network organization's
computing environment to examine computer virus propagation patterns. We
empirically examine a large-scale organizational network consisting of
both social network and technological network. By applying information
retrieval techniques, we map nodes in the social network to nodes in the
technological network to construct the composite network of the
organization. We apply social network analysis to study the topologies
of social and technological networks in this organization. We
statistically test the impact of the interplay between social and
technological network on computer virus propagation using a
susceptible-infective-recovered epidemic process. We find that computer
viruses propagate faster but reach lower level of infection through
technological network than through social network, and viruses propagate
the fastest and reach the highest level of infection through the
composite network. Overlooking the interplay of social network and
technological network underestimates the virus propagation speed and the
scale of infection
Virus Propagation in Multiple Profile Networks
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
and in a network and the nodes of profile
and profile are susceptible to a highly spreading
virus with probabilities and
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
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