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

A New Framework for Centrality Measures in Multiplex Networks