Identifying risks in temporal supernetworks: an IO-SuperPageRank algorithm

• DOI: 10.1057/s41599-024-02823-x
• Publication Date: 2024-02-24T03:02:29Z
• Authors: Yijun Liu; Xiaokun Jin; Yunrui Zhang
• Keywords: Complex system

Abstract Complex systems pose risks characterized by factors such as uncertainty, nonlinearity, and diversity, making traditional risk measurement methods based on a probabilistic framework inadequate. Supernetworks can effectively model complex systems, temporal supernetworks capture the dynamic evolution of these systems. From perspective network stability, aid in identification for In this paper, an IO-SuperPageRank algorithm is proposed supernetwork topological structure. This reveals instability calculating changes node importance, thereby helping to identify To validate effectiveness algorithm, four-layer composed scale-free networks constructed. Simulated experiments are conducted assess impact intralayer edge numbers, interlayer superedge numbers indicator IO value. Linear regression multiple tests were used relationships. The show that three indicators all bring about risks, with having most significant correlation Compared measures centrality connectivity, more accurately predict updates stability. Additionally, paper collected trade data crude oil, chemical light man-made filaments staple fibers from UN Comtrade Database. We constructed supply chain supernetwork, utilizing December 2020 October 2023. study revealed identified brought international situations Russia-Ukraine war, Israel–Hamas conflict, COVID-19 pandemic. demonstrated algorithm’s empirical analysis. future, we plan further expand its application different scenarios, analyzing specific system elements, implement effective intervention measures.