Network reliability analysis through survival signature and machine learning techniques

Shi, Yan ORCID: 0000-0001-8759-0178, Behrensdorf, Jasper ORCID: 0000-0001-9628-7250, Zhou, Jiayan, Hu, Yue ORCID: 0000-0001-8748-7517, Broggi, Matteo ORCID: 0000-0002-3683-3907 and Beer, Michael ORCID: 0000-0002-0611-0345 (2024) Network reliability analysis through survival signature and machine learning techniques. Reliability Engineering & System Safety, 242. p. 109806.

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Abstract

As complex networks become ubiquitous in modern society, ensuring their reliability is crucial due to the potential consequences of network failures. However, the analysis and assessment of network reliability become computationally challenging as networks grow in size and complexity. This research proposes a novel graph-based neural network framework for accurately and efficiently estimating the survival signature and network reliability. The method incorporates a novel strategy to aggregate feature information from neighboring nodes, effectively capturing the response flow characteristics of networks. Additionally, the framework utilizes the higher-order graph neural networks to further aggregate feature information from neighboring nodes and the node itself, enhancing the understanding of network topology structure. An adaptive framework along with several efficient algorithms is further proposed to improve prediction accuracy. Compared to traditional machine learning-based approaches, the proposed graph-based neural network framework integrates response flow characteristics and network topology structure information, resulting in highly accurate network reliability estimates. Moreover, once the graph-based neural network is properly constructed based on the original network, it can be directly used to estimate network reliability of different network variants, i.e., sub-networks, which is not feasible with traditional non-machine learning methods. Several applications demonstrate the effectiveness of the proposed method in addressing network reliability analysis problems.

Item Type:	Article
Uncontrolled Keywords:	7 Affordable and Clean Energy
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	27 Nov 2023 16:07
Last Modified:	15 Mar 2024 05:26
DOI:	10.1016/j.ress.2023.109806
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3177032