Realistic Control of Network Dynamics

By Sean P. Cornelius, W. Kath, Adilson E. Motter. Nature Communications, 2013

339 citations recorded by the source corpus.

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 behaviour or fail. Here we show that it is possible to exploit the same principle to control network behaviour. 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 signalling network of human cancer. Nonlinearity is a hallmark of complex networks, but has generally been regarded as an obstacle to controlling their behaviour. Here Cornelius et al.show how nonlinear dynamics can be harnessed to control a network and drive it to desired states.

Subfields: Biomedical Engineering, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/abf2e9682417124427b513f201d52b66529c9645

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Citations in the corpus

Network resilience (2020, background): “have proposed to bring a network to its desired state through perturbation of the initial state to the attractor basin of desired state [749], as shown in Fig.”
Synchronization in complex networks and its application - A survey of recent advances and challenges (2014, methodology): “, 2013), understanding cancer progression and to develop effective anti-cancer therapies (Cornelius et al., 2013), associate memory (Cornelius et al.”
Target control of complex networks (2014, positive): “According to control theory 8–10 , a dynamic system is controllable if suitable external inputs can move the internal state of the system from any initial state to an arbitrary accessible ﬁnal state in a ﬁnite time interval 11 .”
The fundamental advantages of temporal networks (2016, background): “Consistent with previous ﬁnding that nonlinearity can help control dynamical systems, from driving systems to desired attractors [35] to chaos control [36], our results indicate that nonlinearity induced by temporality can be an asset, rather than an obstacle, to control.”
Control Principles of Complex Networks (2015, methodology): “(b) Systematic design of compensatory perturbations of state variables that take advantage of the full basin of attraction of the desired final state (Cornelius et al., 2013).”