Realistic Control of Network Dynamics

TL;DR

This paper presents a novel framework for controlling complex network dynamics by exploiting nonlinear effects, enabling targeted reprogramming and failure rescue in systems like power grids and cancer signaling networks.

Contribution

It introduces a control method that accounts for nonlinear dynamics and constraints, allowing effective intervention even when direct access to target states is limited.

Findings

Successfully reprogrammed a power-grid network to prevent cascading failures.

Identified potential drug targets in a human cancer signaling network.

Demonstrated control of network behavior under realistic constraints.

Abstract

The control of complex networks is of paramount importance in areas as diverse as ecosystem management, emergency response, and cell reprogramming. A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behavior or fail. Here, we show that it is possible to exploit the same principle to control network behavior. Our approach accounts for the nonlinear dynamics inherent to real systems, and allows bringing the system to a desired target state even when this state is not directly accessible due to constraints that limit the allowed interventions. Applications show that this framework permits reprogramming a network to a desired task as well as rescuing networks from the brink of failure---which we illustrate through the mitigation of cascading failures in a power-grid network and the identification of potential drug targets in a signaling network of human cancer.