Robust expansion of networks against cascading failures with reinforcement learning

Yu Wu (), Cunlai Pu and Yongxiang Xia ()
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Yu Wu: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, P. R. China
Cunlai Pu: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, P. R. China
Yongxiang Xia: School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, P. R. China

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 11, 1-12

Abstract: The network infrastructures, such as the power grids and Internet, are expanding in size due to the increasing needs of our society. This brings about the problem of expanding networks with a guarantee of robustness against network disturbances that may cause catastrophic consequences. In this paper, we study the optimal network expansion in terms of network robustness against cascading failures. Specifically, we consider the network expansion as a Markovian decision process and further propose a reinforcement learning based network expansion method. Simulation results in model networks and real-world networks demonstrate that our expansion method can greatly improve network robustness. Our work provides some insights for the optimal expansion of network infrastructures.

Keywords: Network expansion; cascading failure; network robustness; reinforcement learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0129183124501353

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