Network reliability analysis through survival signature and machine learning techniques

authored by

Yan Shi, Jasper Behrensdorf, Jiayan Zhou, Yue Hu, Matteo Broggi, Michael Beer

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.

Organisation(s)

Faculty of Civil Engineering and Geodetic Science
Institute for Risk and Reliability
CRC 871 Regeneration of Complex Capital Goods

External Organisation(s)

Hohai University

Type

Article

Journal

Reliability engineering & system safety

Volume

242

ISSN

0951-8320

Publication date

02.2024

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.2023.109806 (Access: Closed)