A resilient network recovery framework against cascading failures with deep graph learning

Author:

Zhou Jian1ORCID,Zheng Weijian2,Wang Dali3,Coit David W.4ORCID

Affiliation:

1. School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China

2. Department of Computer and Information Science, Indiana University-Purdue University, Indianapolis, IN, USA

3. Energy and Environmental Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA

4. Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, USA

Abstract

Because of the increasing importance and dependencies of infrastructure networks and the potential for massive cascading failures in real-world network systems, maintenance optimization to effectively reduce system performance loss caused by diverse disruptions is of significant interest among researchers and practitioners. In this work, a new recovery framework was developed to rapidly identify important system components for maintenance to improve network resilience against cascading failures. This work provides distinct advantages to determine an optimal maintenance priority by combining real-time network structure importance with other maintenance prioritization based on customer preference. This approach adopts structural graph embedding and deep reinforcement learning to extract real-time network topology information (such as minimum vertex cover) to update the maintenance priority during the recovery process. Based on the case studies on synthetic networks and a US airport network, the proposed recovery framework with real-time network topology awareness shows better performance than other maintenance prioritization strategies regarding resilience enhancement. This work improves the understanding of how the changing network structure influences maintenance effects. It also provides insights of the practical usefulness of advanced deep learning on helping optimal maintenance prioritization to effectively reduce the intensity and extent of cascading failures.

Publisher

SAGE Publications

Subject

Safety, Risk, Reliability and Quality

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