  We consider the problem of identifying the most influential nodes for a
spreading process on a network when prior knowledge about structure and
dynamics of the system is incomplete or erroneous. Specifically, we perform a
numerical analysis where the set of top spreaders is determined on the basis of
prior information that is artificially altered by a certain level of noise. We
then measure the optimality of the chosen set by measuring its spreading impact
in the true system. Whereas we find that the identification of top spreaders is
optimal when prior knowledge is complete and free of mistakes, we also find
that the quality of the top spreaders identified using noisy information
doesn't necessarily decrease as the noise level increases. For instance, we
show that it is generally possible to compensate for erroneous information
about dynamical parameters by adding synthetic errors in the structure of the
network. Further, we show that, in some dynamical regimes, even completely
losing prior knowledge on network structure may be better than relying on
certain but incomplete information.
