NECO - A scalable algorithm for NEtwork COntrol

TL;DR

This paper introduces NECO, a scalable algorithm for controlling complex networks and nonlinear dynamical systems through compensatory perturbations, enabling targeted state transitions under constraints with efficient computation.

Contribution

The paper presents NECO, a novel scalable algorithm that identifies control interventions in high-dimensional systems using compensatory perturbations, with ready-to-use software implementation.

Findings

Algorithm scales as variables^2.5, enabling control of large systems.

Software can be applied to systems described by coupled differential equations.

Effective in driving systems to desired states despite constraints.

Abstract

We present an algorithm for the control of complex networks and other nonlinear, high-dimensional dynamical systems. The computational approach is based on the recently-introduced concept of compensatory perturbations -- intentional alterations to the state of a complex system that can drive it to a desired target state even when there are constraints on the perturbations that forbid reaching the target state directly. Included here is ready-to-use software that can be applied to identify eligible control interventions in a general system described by coupled ordinary differential equations, whose specific form can be specified by the user. The algorithm is highly scalable, with the computational cost scaling as the number of dynamical variables to the power 2.5.