  Multiplex networks are generalized network structures that are able to
describe networks in which the same set of nodes are connected by links that
have different connotations. Multiplex networks are ubiquitous since they
describe social, financial, engineering and biological networks as well.
Extending our ability to analyze complex networks to multiplex network
structures increases greatly the level of information that is possible to
extract from Big Data. For these reasons characterizing the centrality of nodes
in multiplex networks and finding new ways to solve challenging inference
problems defined on multiplex networks are fundamental questions of network
science. In this paper we discuss the relevance of the Multiplex PageRank
algorithm for measuring the centrality of nodes in multilayer networks and we
characterize the utility of the recently introduced indicator function
$\widetilde{\Theta}^{S}$ for describing their mesoscale organization and
community structure. As working examples for studying these measures we
consider three multiplex network datasets coming for social science.
