用动态图学习追踪金融风险传播，识别隐藏威胁。
Utilizing Effective Dynamic Graph Learning to Shield Financial Stability from Risk Propagation
- 构建动态图模块，跨时序与空间维度增强信息学习
- 利用风险聚类特性提升对隐性风险的识别率
- 可视化风险传播路径，适合金融风控与监管人员
金融风险可在紧密耦合的时间与空间维度上传播，严重威胁金融稳定。此外，未标记数据中的风险往往难以察觉。为此，我们提出GraphShield，具有三大创新：增强跨域信息学习：设计动态图学习模块，提升时间与空间维度上的信息融合能力；先进风险识别：基于风险的聚类特性，构建风险识别模块，强化对隐性威胁的探测；风险传播可视化：提供可视化工具，量化并验证引发广泛级联风险的关键节点。在两个真实世界及两个开源数据集上的大量实验表明，该框架表现出稳健性能。本方法代表了利用人工智能提升金融稳定性的重大进展，为缓解金融网络内风险扩散提供了有力解决方案。
原文摘要 · Abstract (English)
Financial risks can propagate across both tightly coupled temporal and spatial dimensions, posing significant threats to financial stability. Moreover, risks embedded in unlabeled data are often difficult to detect. To address these challenges, we introduce GraphShield, a novel approach with three key innovations: Enhanced Cross-Domain Infor mation Learning: We propose a dynamic graph learning module to improve information learning across temporal and spatial domains. Advanced Risk Recognition: By leveraging the clustering characteristics of risks, we construct a risk recognizing module to enhance the identification of hidden threats. Risk Propagation Visualization: We provide a visualization tool for quantifying and validating nodes that trigger widespread cascading risks. Extensive experiments on two real-world and two open-source datasets demonstrate the robust performance of our framework. Our approach represents a significant advancement in leveraging artificial intelligence to enhance financial stability, offering a powerful solution to mitigate the spread of risks within financial networks.
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