  Multilayer networks allow for modeling complex relationships, where
individuals are embedded in multiple social networks at the same time. Given
the ubiquity of such relationships, these networks have been increasingly
gaining attention in the literature. This paper presents the first analysis of
the robustness of centrality measures against strategic manipulation in
multilayer networks. More specifically, we consider an "evader" who
strategically chooses which connections to form in a multilayer network in
order to obtain a low centrality-based ranking-thereby reducing the chance of
being highlighted as a key figure in the network-while ensuring that she
remains connected to a certain group of people. We prove that determining an
optimal way to "hide" is NP-complete and hard to approximate for most
centrality measures considered in our study. Moreover, we empirically evaluate
a number of heuristics that the evader can use. Our results suggest that the
centrality measures that are functions of the entire network topology are more
robust to such a strategic evader than their counterparts which consider each
layer separately.
