Spectral Representations of Graphons in Very Large Network Systems Control

TL;DR

This paper explores spectral representations of graphons, providing eigenfunction approximations and applications to control of large network systems, including controllability analysis and real-world network data insights.

Contribution

It introduces spectral analysis techniques for graphons, including eigenfunction approximations, and applies them to control problems in large and growing networks.

Findings

Spectral properties of graphons are characterized.

Low-dimensional spectral approximations are feasible for real networks.

Spectral analysis can inform control strategies for epidemic spread.

Abstract

Graphon-based control has recently been proposed and developed to solve control problems for dynamical systems on networks which are very large or growing without bound (see Gao and Caines, CDC 2017, CDC 2018). In this paper, spectral representations, eigenfunctions and approximations of graphons, and their applications to graphon-based control are studied. First, spectral properties of graphons are presented and then approximations based on Fourier approximated eigenfunctions are analyzed. Within this framework, two classes of graphons with simple spectral representations are given. Applications to graphon-based control analysis are next presented; in particular, the controllability of systems distributed over very large networks is expressed in terms of the properties of the corresponding graphon dynamical systems. Moreover, spectral analysis based upon real-world network data is presented, which demonstrates that low-dimensional spectral approximations of networks are possible. Finally, an initial, exploratory investigation of the utility of the spectral analysis methodology in graphon systems control to study the control of epidemic spread is presented.