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• W4316670328 startingPage "1129" @default.
• W4316670328 abstract "The methods of complex networks have been extensively used to characterize information flow in complex systems, such as risk propagation in complex financial networks. However, network dynamics are ignored in most cases despite systems with similar topological structures exhibiting profoundly different dynamic behaviors. To observe the spatiotemporal patterns of risk propagation in complex financial networks, we combined a dynamic model with empirical networks. Our analysis revealed that hub nodes play a dominant role in risk propagation across the network and respond rapidly, thus exhibiting a degree-driven effect. The influence of key dynamic parameters, i.e., infection rate and recovery rate, was also investigated. Furthermore, the impacts of two typical characteristics of complex financial systems—the existence of community structures and frequent large fluctuations—on the spatiotemporal patterns of risk propagation were explored. About 30% of the total risk propagation flow of each community can be explained by the top 10% nodes. Thus, we can control the risk propagation flow of each community by controlling a few influential nodes in the community and, in turn, control the whole network. In extreme market states, hub nodes become more dominant, indicating better risk control." @default.
• W4316670328 created "2023-01-17" @default.
• W4316670328 creator A5008986543 @default.
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• W4316670328 date "2023-01-14" @default.
• W4316670328 modified "2023-10-18" @default.
• W4316670328 title "Spatiotemporal Patterns of Risk Propagation in Complex Financial Networks" @default.
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• W4316670328 doi "https://doi.org/10.3390/app13021129" @default.
• W4316670328 hasPublicationYear "2023" @default.
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