Nonparametric identification of Kronecker networks

Abstract

We address the problem to estimate a dynamic network whose edges describe Granger causality relations and whose topology has a Kronecker structure. Such a structure arises in many real networks and allows to understand the organization of complex networks. We propose a kernel-based PEM method to learn such networks. Numerical examples show the effectiveness of the proposed method.