  Existing socio-psychological studies suggest that users of a social network
form their opinions relying on the opinions of their neighbors. According to
DeGroot opinion formation model, one value of particular importance is the
asymptotic consensus value---the sum of user opinions weighted by the users'
eigenvector centralities. This value plays the role of an attractor for the
opinions in the network and is a lucrative target for external influence.
However, since any potentially malicious control of the opinion distribution in
a social network is clearly undesirable, it is important to design methods to
prevent the external attempts to strategically change the asymptotic consensus
value.
  In this work, we assume that the adversary wants to maximize the asymptotic
consensus value by altering the opinions of some users in a network; we, then,
state DIVER---an NP-hard problem of disabling such external influence attempts
by strategically adding a limited number of edges to the network. Relying on
the theory of Markov chains, we provide perturbation analysis that shows how
eigenvector centrality and, hence, DIVER's objective function change in
response to an edge's addition to the network. The latter leads to the design
of a pseudo-linear-time heuristic for DIVER, whose computation relies on
efficient estimation of mean first passage times in a Markov chain. We confirm
our theoretical findings in experiments.
