  We develop a framework to track the structure of temporal networks with a
signal processing approach. The method is based on the duality between networks
and signals using a multidimensional scaling technique. This enables a study of
the network structure using frequency patterns of the corresponding signals. An
extension is proposed for temporal networks, thereby enabling a tracking of the
network structure over time. A method to automatically extract the most
significant frequency patterns and their activation coefficients over time is
then introduced, using nonnegative matrix factorization of the temporal
spectra. The framework, inspired by audio decomposition, allows transforming
back these frequency patterns into networks, to highlight the evolution of the
underlying structure of the network over time. The effectiveness of the method
is first evidenced on a toy example, prior being used to study a temporal
network of face-to-face contacts. The extraction of sub-networks highlights
significant structures decomposed on time intervals.
