  Evolving multiplex networks are a powerful model for representing the
dynamics along time of different phenomena, such as social networks, power
grids, biological pathways. However, exploring the structure of the multiplex
network time series is still an open problem. Here we propose a two-steps
strategy to tackle this problem based on the concept of distance (metric)
between networks. Given a multiplex graph, first a network of networks is built
for each time steps, and then a real valued time series is obtained by the
sequence of (simple) networks by evaluating the distance from the first element
of the series. The effectiveness of this approach in detecting the occurring
changes along the original time series is shown on a synthetic example first,
and then on the Gulf dataset of political events.
