Learning local modules in dynamic networks

Over the last decade, the problem of data-driven modeling in linear dynamic networks has been introduced in the literature, and has shown to contain many different challenging research questions. The structural and topological properties of networks become a central ingredient in the data-driven modeling problem, as well as the selection of locations for signals to be sensed and for excitation signals to be added. In this survey-type paper we will present an overview of recent results that are obtained for the problem of learning the dynamics of a single link/module in a dynamic network of which the topology is given. The surveyed methods include extensions of classical identification methods, combined with Bayesian kernel-based methods. Particular attention will be given to the selection of signals that need to be available for measurement/excitation, and accuracy properties of the estimated models in terms of consistency and minimum variance properties.