Response flow graph neural network for capacitated network reliability analysis

Authored by

Yan Shi, Cheng Liu, Michael Beer, Hong Zhong Huang, Yu Liu

Abstract

Capacitated network reliability (CNR) analysis is essential for computing the reliability of diverse networks. The NP-hard nature of CNR problems makes exact solutions through exhaustive permutations impractical for many real-world engineering networks. In this research, a new graph-based neural network termed the response flow graph neural network (RFGNN) is developed to address CNR problems. The innovation of the proposed method comprises three key components. Firstly, an iteration equation is proposed to update network link weights by identifying nodes where flow is obstructed during propagation. Secondly, a novel expression is developed to amalgamate local neighborhood information for each node by incorporating the updated link weights, culminating in the creation of the RFGNN. Thirdly, an adaptive framework is developed to improve the prediction accuracy of the RFGNN in solving CNR problems. Several CNR problems are presented to assess the efficacy of the developed method. The results unequivocally demonstrate the effectiveness of the developed method. Furthermore, the RFGNN exhibits remarkable computational accuracy when estimating CNRs across various sub-networks once it is appropriately constructed from the original network. This represents a capability that conventional non-machine learning methods typically struggle to attain.

Details

Organisation(s): Institute for Risk and Reliability
External Organisation(s): City University of Hong Kong
University of Liverpool
Tongji University
University of Electronic Science and Technology of China
Type: Article
Journal: Reliability Engineering and System Safety
Volume: 262
No. of pages: 15
ISSN: 0951-8320
Publication date: 10.2025
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Safety, Risk, Reliability and Quality, Industrial and Manufacturing Engineering
Electronic version(s): https://doi.org/10.1016/j.ress.2025.111198 (Access: Closed )