Enabling controlling complex networks with local topological information
Stanley, H. Eugene
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Citation (published version)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
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.
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