  When dealing with evolving or multi-dimensional complex systems, network
theory provides with elegant ways of describing their constituting components,
through respectively time-varying and multi-layer complex networks.
Nevertheless, the analysis of how these components are related is still an open
problem. We here propose a framework for analysing the evolution of a (complex)
system, by describing the structure created by the difference between multiple
networks by means of the Information Content metric. As opposed to other
approaches, as for instance the use of global overlap or entropies, the
proposed one allows to understand if the observed changes are due to random
noise, or to structural (targeted) modifications. We validate the framework by
means of sets of synthetic networks, as well as networks representing real
technological, social and biological evolving systems. We further propose a way
of reconstructing network correlograms, which allow to convert the system's
evolution to the frequency domain.
