Paper: arXiv 2502.13979

Authors: Guanyuan Yu, Qing Li, Yu Zhao, Jun Wang, YiJun Chen, Shaolei Chen

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel dynamic graph neural network architecture (GraphShield) that interleaves spatial and temporal operations with kernelized linear-time attention for risk propagation modeling. Its theoretical formulation is paired with empirical validation across two real-world and two open-source datasets.

Research Flowchart

  flowchart TD
    A[Research Goal: Detect Hidden Risks & Mitigate Spatiotemporal Propagation] --> B[Input Data: Financial Networks & Cross-Asset Datasets]
    B --> C[GraphShield Framework]
    
    subgraph C [GraphShield Framework]
        C1[Dynamic Graph Learning Module: Temporal & Spatial Modeling]
        C2[Risk Recognizing Module: Risk Clustering]
        C3[Risk Propagation Visualization: Quantify Cascading Effects]
    end
    
    C --> D[Outcomes: Improved Hidden Threat Identification & Risk Mitigation]

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0 of 75 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.