Enabling controlling complex networks with local topological information
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Date
2018-03-15
Authors
Li, Guoqi
Deng, Lei
Xiao, Gaoxi
Tang, Pei
Wen, Changyun
Hu, Wuhua
Pei, Jing
Shi, Luping
Stanley, H. Eugene
Version
Published version
OA Version
Citation
Guoqi Li, Lei Deng, Gaoxi Xiao, Pei Tang, Changyun Wen, Wuhua Hu, Jing Pei, Luping Shi, H Eugene Stanley. 2018. "Enabling Controlling Complex Networks with Local Topological Information." Scientific Reports, Volume 8. https://doi.org/10.1038/s41598-018-22655-5
Abstract
Complex networks characterize the nature of internal/external interactions in real-world systems
including social, economic, biological, ecological, and technological networks. Two issues keep as
obstacles to fulflling control of large-scale networks: structural controllability which describes the
ability to guide a dynamical system from any initial state to any desired fnal state in fnite time, with a
suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost
for driving the network to a predefned state with a given number of control inputs. For large complex
networks without global information of network topology, both problems remain essentially open.
Here we combine graph theory and control theory for tackling the two problems in one go, using only
local network topology information. For the structural controllability problem, a distributed local-game
matching method is proposed, where every node plays a simple Bayesian game with local information
and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity.
Starring from any structural controllability solution, a minimizing longest control path method can
efciently reach a good solution for the optimal control in large networks. Our results provide solutions
for distributed complex network control and demonstrate a way to link the structural controllability and
optimal control together.
Description
License
© The Author(s) 2018. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.