Fourier transform for signals on dynamic graphs

Signal processing on graphs offers a new way of analyzing multivariate signals. The different relationships among the sources generating the multivariate signals can be captured by weighted graphs where the nodes are the signal sources and the edges correspond to the relationships between these signals. Classical signal processing concepts need to be adapted to signals on graphs. In this paper, we propose a graph Fourier transform for signals on dynamic graphs, where the relationships vary over time. The proposed transform is evaluated on both simulated and real dynamic social networks with signal defined on its nodes.