Today we had an interesting seminar by Sara based on two of her papers. Thanks for the nice presentation!

Abstract: Network science is a fundamental framework to study and understand complex systems in a large variety of phenomena in disciplines like physics, economics, biology and sociology. Dynamics on  networks are usually described by defining a linear dynamical model and  the properties of such systems can be derived, for each case, by the  spectrum of a specific matrix. The characterization of a linear system by its spectrum is recognized but its not sufficiently precise when the  linear operator is non-normal. A linear system, in an arbitrary  dimension, is non-normal when its governing matrix does not commute with  its conjugate transpose. Although a non-normal system can initially be  close to an asympotically stable equilibrium, it can leave this state  even when a moderate external perturbation occurs and this effect is  more notable once one includes stochastic forces to the model. In this  perspective, non-normality is an interesting property to investigate  stochastic effects like pattern formation or the emerging of stochastic  quasicycles.