Dynamical robustness of complex networks subject to long-range connectivity

TL;DR

This paper investigates how long-range interactions influence the dynamical robustness of complex networks and proposes a self-feedback mechanism to enhance network resilience across various topologies.

Contribution

It introduces the concept that long-range interactions affect robustness and presents a self-feedback method to restore rhythmicity and improve resilience in diverse network types.

Findings

Long-range interactions significantly impact network robustness.

Self-feedback can effectively restore global rhythmicity.

Analytical results align well with numerical simulations.

Abstract

In spite of a few attempts in understanding the dynamical robustness of complex networks, this extremely important subject of research is still in its dawn as compared to the other dynamical processes on networks. We hereby consider the concept of long-range interactions among the dynamical units of complex networks and demonstrate for the first time that such a characteristic can have noteworthy impacts on the dynamical robustness of networked systems, regardless of the underlying network topology. We present a comprehensive analysis of this phenomenon on top of diverse network architectures. Such dynamical damages being able to substantially affect the network performance, determining mechanisms that boost the robustness of networks becomes a fundamental question. In this work, we put forward a prescription based upon self-feedback that can efficiently resurrect global rhythmicity of complex networks composed of active and inactive dynamical units, and thus can enhance the network robustness. We have been able to delineate the whole proposition analytically while dealing with all d-path adjacency matrices, having an excellent agreement with the numerical results. For the numerical computations, we examine scale-free networks, Watts-Strogatz small world model and also Erd\"os-R\'enyi random network, along with Landau-Stuart oscillators for casting the local dynamics.