Nowadays graphs have become of significant importance, given their use to describe complex system dynamics, with notable applications to real world problems, e.g. graph representation of the brain, social networks, biological networks and spreading of a disease, etc.. The intuitive and simple definition of a graph, by means of nodes, weighted edges between nodes and adjacency matrix, has led to a continuous expansion of this research field in the last decades by giving  birth to innovative frameworks and interdisciplinary and multidisciplinary research. Certainly, the possibility of defining and studying signals on a graph has played a central role in the ever-increasing research on graphs.

The main goal of TIGRECO (TIme-varying signals on Graphs: REal and COmplex methods) is to introduce new useful mathematical methods to process time-varying signals on graphs. We would like to focus on this aspect both from a theoretical and numerical point of view and by means of concrete applications. A possible application is related to the study of signals such as fMRI (functional Magnetic Resonance Imaging) time series used to better understand human brain activity.