Network Effects on Robustness of Dynamic Systems

TL;DR

This paper reviews how network structure influences the robustness of dynamic systems across static and dynamic regimes, emphasizing recent theoretical results and identifying future research directions.

Contribution

It synthesizes current understanding of network effects on robustness and highlights areas where system theoretic tools provide tight guarantees, guiding future investigations.

Findings

Physical constraints affect static network robustness.

Small gain analysis offers tight robustness bounds for linear dynamics.

Nonlinear techniques help understand cascading failures.

Abstract

We review selected results related to robustness of networked systems in finite and asymptotically large size regimes, under static and dynamical settings. In the static setting, within the framework of flow over finite networks, we discuss the effect of physical constraints on robustness to loss in link capacities. In the dynamical setting, we review several settings in which small gain type analysis provides tight robustness guarantees for linear dynamics over finite networks towards worst-case and stochastic disturbances. We also discuss network flow dynamic settings where nonlinear techniques facilitate in understanding the effect on robustness of constraints on capacity and information, substituting information with control action, and cascading failure. We also contrast the latter with a representative contagion model. For asymptotically large networks, we discuss the role of network properties in connecting microscopic shocks to emergent macroscopic fluctuations under linear dynamics as well as for economic networks at equilibrium. Through the review of these results, the paper aims to achieve two objectives. First, to highlight selected settings in which the role of interconnectivity structure of a network on its robustness is well-understood. Second, to highlight a few additional settings in which existing system theoretic tools give tight robustness guarantees, and which are also appropriate avenues for future network-theoretic investigations.