Temporal Attentive Graph Networks for Financial Surveillance: An Incremental Multi-Scale Framework

Addressing the limitations of conventional financial time series models in capturing modern market dynamics, this study introduces a novel financial monitoring framework based on Temporal Attentive Graph Networks (TAGN). The TAGN model integrates Graph Attention Networks (GAT) and Gated Recurrent Units (GRU) to simultaneously capture spatial dependencies among financial entities and the dynamic evolution of network structure. The framework uses a multi-relational dynamic graph of 50 NASDAQ stocks. Node features fuse multi-scale information (micro, meso, macro), and the graph structure integrates price correlation, supply chain, and institutional co-holdings. Compared to benchmarks (Logistic Regression, GCN, GCN-LSTM), TAGN demonstrates superior performance (AUC: 0.89) in predicting stocks likely to experience extreme volatility (>±15%) within three months. Empirical analysis (2018–2023) effectively quantifies network imprints of major events (e.g., 45% average edge weight surge during COVID-19; community stability Jaccard similarity drop to 0.34 following the SVB incident). An ablation study confirms the critical contribution of the attention and temporal components. Finally, by generating a risk early-warning index integrating network topology and model output, this research offers a new tool for systemic risk quantification and proactive anticipation.