  The non-trivial structure of such complex systems makes the analysis of their
collective behavior a challenge. The problem is even more difficult when the
information is distributed across networks (e.g., communication networks in
different media); in this case, it becomes impossible to have a complete, or
even partial picture, if situations are analyzed separately within each network
due to sparsity. A multiplex network is well-suited to model the complexity of
this kind of systems by preserving the semantics associated with each network.
Centrality measures are fundamental for the identification of key players, but
existing approaches are typically designed to capture a predefined aspect of
the system, ignoring or merging the semantics of the individual layers.
