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.

Most networked systems of scientific interest are characterized by temporal links, meaning the network's structure changes over time. Link temporality has been shown to hinder many dynamical processes, from information spreading to accessibility, by disrupting network paths. Considering the ubiquity of temporal networks in nature, we ask: Are there any advantages of the networks' temporality? We use an analytical framework to show that temporal networks can, compared to their static counterparts, reach controllability faster, demand orders of magnitude less control energy, and have control trajectories, that are considerably more compact than those characterizing static networks. Thus, temporality ensures a degree of flexibility that would be unattainable in static networks, enhancing our ability to control them.

Free-water measurements of dissolved oxygen (DO) in lakes are becoming common and provide opportunities for estimating ecosystem processes, such as gross primary production (GPP) and ecosystem respiration (R). The models used to estimate metabolism often subsume biological processes into one parameter each for GPP and R. However, high-frequency DO observations made over days show diverse patterns at multiple time scales, suggesting a complex suite of processes controls DO dynamics. Can we improve metabolism estimates and predictive ability for DO at diel scales by adding complexity to the models? In this study, we use data from two north temperate lakes to test a variety of metabolism models representing a suite of non-linear metabolic processes. To test whether alternative models can be discriminated, we simulated DO with assumed parameter values and auto-regressive noise, and fit the models to the simulated DO. The most complex model could be discriminated from simpler models and provided the most accurate and precise predictions. However, when models were fit to observed DO data from the sensor network, the simplest model predicted DO as well as the most complex one. The added complexity did not improve model performance. An analysis of the model residuals indicates that physics may explain some of the DO pattern not predicted, especially high-frequency oscillations and anomalies that appear to coincide with weather patterns. Under reasonably stable weather conditions and at scales of a few days, simple metabolism models explain the bulk of diel DO variability.