Abstract

Joint work with Ronan Hamon (LIF, Marseille, France), P. Flandrin (CNRS, LP, ENS de Lyon, France) and C. Robardet (LIRIS, INSA de Lyon, France)
We have proposed  a  new framework to track the structure of temporal networks, using a signal processing approach: the method is based on the duality between static networks and signals using a multidimensional scaling technique. For temporal networks, it enables a tracking of the network structure over time. To extract the most significant patterns of the networks and their activation over time, we use nonnegative matrix factorization of the temporal spectra. This framework, inspired by audio decomposition, allows transforming back these frequency patterns into networks, so as 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.