  The study of complex networks has been historically based on simple graph
data models representing relationships between individuals. However, often
reality cannot be accurately captured by a flat graph model. This has led to
the development of multi-layer networks. These models have the potential of
becoming the reference tools in network data analysis, but require the parallel
development of specific analysis methods explicitly exploiting the information
hidden in-between the layers and the availability of a critical mass of
reference data to experiment with the tools and investigate the real-world
organization of these complex systems. In this work we introduce a real-world
layered network combining different kinds of online and offline relationships,
and present an innovative methodology and related analysis tools suggesting the
existence of hidden motifs traversing and correlating different representation
layers. We also introduce a notion of betweenness centrality for multiple
networks. While some preliminary experimental evidence is reported, our
hypotheses are still largely unverified, and in our opinion this calls for the
availability of new analysis methods but also new reference multi-layer social
network data.
