  Real-world complex systems exhibit multiple levels of relationships. In many
cases they require to be modeled as interconnected multilayer networks,
characterizing interactions of several types simultaneously. It is of crucial
importance in many fields, from economics to biology and from urban planning to
social sciences, to identify the most (or the less) influential nodes in a
network using centrality measures. However, defining the centrality of actors
in interconnected complex networks is not trivial. In this paper, we rely on
the tensorial formalism recently proposed to characterize and investigate this
kind of complex topologies, and extend two well known random walk centrality
measures, the random walk betweenness and closeness centrality, to
interconnected multilayer networks. For each of the measures we provide
analytical expressions that completely agree with numerically results.
