  Real-world complex systems exhibit multiple levels of relationships. In many
cases, they require to be modeled by interconnected multilayer networks,
characterizing interactions on several levels simultaneously. It is of crucial
importance in many fields, from economics to biology, from urban planning to
social sciences, to identify the most (or the less) influent nodes in a
network. However, defining the centrality of actors in an interconnected
structure is not trivial.
  In this paper, we capitalize on the tensorial formalism, recently proposed to
characterize and investigate this kind of complex topologies, to show how
several centrality measures -- well-known in the case of standard ("monoplex")
networks -- can be extended naturally to the realm of interconnected
multiplexes. We consider diagnostics widely used in different fields, e.g.,
computer science, biology, communication and social sciences, to cite only some
of them. We show, both theoretically and numerically, that using the weighted
monoplex obtained by aggregating the multilayer network leads, in general, to
relevant differences in ranking the nodes by their importance.
