Details
| Original language | English |
|---|---|
| Article number | 109806 |
| Journal | Reliability engineering & system safety |
| Volume | 242 |
| Early online date | 11 Nov 2023 |
| Publication status | Published - Feb 2024 |
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.
Keywords
- Adaptive framework, Graph-based neural network, Machine learning, Network reliability, Survival signature
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Engineering(all)
- Industrial and Manufacturing Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Reliability engineering & system safety, Vol. 242, 109806, 02.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Network reliability analysis through survival signature and machine learning techniques
AU - Shi, Yan
AU - Behrensdorf, Jasper
AU - Zhou, Jiayan
AU - Hu, Yue
AU - Broggi, Matteo
AU - Beer, Michael
N1 - This work is supported by the National Natural Science Foundation of China (Grant 52205252, and 72171194), and the National Natural Science Foundation of Sichuan Province (Grant 2023NSFSC0876). The first author would also thanks for the support of the Alexander von Humboldt Foundation of Germany.
PY - 2024/2
Y1 - 2024/2
N2 - 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.
AB - 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.
KW - Adaptive framework
KW - Graph-based neural network
KW - Machine learning
KW - Network reliability
KW - Survival signature
UR - http://www.scopus.com/inward/record.url?scp=85177987789&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2023.109806
DO - 10.1016/j.ress.2023.109806
M3 - Article
VL - 242
JO - Reliability engineering & system safety
JF - Reliability engineering & system safety
SN - 0951-8320
M1 - 109806
ER -