计算机与现代化Issue(5)：27-32,6.DOI:10.3969/j.issn.1006-2475.2024.05.006
基于图神经网络的多层银企网络融合研究
Multi-layer Bank-enterprise Converged Network Based on Graph Neural Network
摘要
Abstract
The potential systemic risk in the financial industry is difficult to be accurately identified.Based on the loan data of the direct systemic risk contagion channel and internet text information of the indirect channel,a multi-layer bank-enterprise network is constructed,and a multi-layer bank-enterprise network convergence model is designed by using graph convolutional neural networks(GCN).Based on the converged network,this paper quantitatively evaluates the systemic risk contagion process of 29 banks and 75 real estate institutions.The converged network analysis shows that the systemic risk transmission capacity un-der the joint impact of multi-layer bank-enterprise network is significantly greater than the systemic risk of single or two-layer network,and the systemic risk of the inter-enterprise network based on the indirect channel is more obvious.Financial pruden-tial supervision should pay more attention to the ability of data analysis,deep learning and other technologies to integrate big data financial resources and effectively improve the level of risk monitoring and warning.关键词
多层网络融合/系统性风险传染/图卷积神经网络/文本分析Key words
convergence of multi-layer network/systemic risk contagion/graph convolutional neural network/text analysis分类
管理科学引用本文复制引用
李珊,王林娜,高丁佳,宣海波..基于图神经网络的多层银企网络融合研究[J].计算机与现代化,2024,(5):27-32,6.基金项目
国家社会科学基金资助项目(17BGL055) （17BGL055）
中央高校基本科研业务费专项资金资助项目(ND2023005) （ND2023005）
南京航空航天大学科研与实践创新计划项目(xcxjh20220904) （xcxjh20220904）
