A novel methodology concentrating on risk propagation to conduct a risk analysis based on a directed complex network

A novel methodology is proposed in the present study to describe the risk propagation process by quantitatively evaluating the criticality and sensitivity of risk events according to complex network theory, based on which risk matrices are developed to interrupt the risk propagation process by setting up safety barriers. The applicability and accuracy of the improved k-shell decomposition algorithm and risk flow model for calculating the criticality proposed in this study are verified by the susceptible-infected-recovered (SIR) simulation, which is widely regarded as a benchmark for complex networks (CN) issues. The results confirm the advantages of the proposed methodologies considering comprehensively various comparison indicators. The sensitivity of the nodes is quantified by running an SIR simulation with a variable infection rate and recovery rate. Finally, the criticality and sensitivity of risk events contribute to the development of risk matrices with three different risk scenarios, based on which the applicability and effectiveness of safety barriers are qualitatively analyzed to interrupt the risk propagation process. The framework and methodologies proposed in this study could well present the risk propagation process within CNs and are proven to have a great potential for studies on safety barriers.

Automated summary

This study proposes a novel methodology based on complex network theory to describe the risk propagation process and develop risk matrices to interrupt the risk propagation. The applicability and accuracy of the proposed method are verified through SIR simulation and comprehensive comparison indicators. The sensitivity of nodes and the applicability and effectiveness of safety barriers are quantitatively and qualitatively analyzed.