Exact controllability of complex networks

By Zhengzhong Yuan, Chen Zhao, Zengru Di, Wenxu Wang, Ying-Cheng Lai. Nature Communications, 2013

555 citations recorded by the source corpus.

Controlling complex networks is of paramount importance in science and engineering. Despite the recent development of structural controllability theory, we continue to lack a framework to control undirected complex networks, especially given link weights. Here we introduce an exact controllability paradigm based on the maximum multiplicity to identify the minimum set of driver nodes required to achieve full control of networks with arbitrary structures and link-weight distributions. The framework reproduces the structural controllability of directed networks characterized by structural matrices. We explore the controllability of a large number of real and model networks, finding that dense networks with identical weights are difficult to be controlled. An efficient and accurate tool is offered to assess the controllability of large sparse and dense networks. The exact controllability framework enables a comprehensive understanding of the impact of network properties on controllability, a fundamental problem towards our ultimate control of complex systems. Although it has been possible to calculate the conditions for exerting complete control over a directed complex network, for undirected and weighted networks this calculation is inexact. Yuan et al. develop a general framework for determining the controllability of any complex network.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0fc67092ad5da179ffb27c5dab9101f03446ad7e

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Control Principles of Complex Networks (2015, background): “12), allowing us to explore the impact of the network topology and link-weight distributions on ND (Yuan et al., 2013).”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, methodology): “An alternative framework, the exact controllability framework, was subsequently formulated [426], which was based on the principle of maximum multiplicity to identify the minimum set of driver nodes required to achieve full control of networks with arbitrary structures and link weight distributions.”
Synchronization in complex networks and its application - A survey of recent advances and challenges (2014, methodology): “Structural controllability (Liu et al., 2011) or exact controllability (Yuan et al., 2013) mainly focuses on linear time-invariant systems.”