Contagion Path Prediction in Financial Enterprise Networks Using a Starling Murmuration Optimized Dual-Encoder Self-Attention GNN (SM-ISAGNN) - Jiang, Renrong (Informatica, 2025)
Financial risk contagion refers to the cascading spread of financial distress among interconnected entities within a networked system, posing serious threats to economic stability. Predicting the pathways through which such contagion propagates is essential for early intervention and systemic risk mitigation. This research proposes a novel contagion path prediction algorithm based on a deep learning (DL) framework, designed to capture both the direct and indirect transmission of financial risks across enterprise networks. The Starling Murmuration Optimizer-driven improved Self-Attention Graph Neural Network (SM-ISAGNN) is applied to predict financial risk contagion paths in enterprise networks. The model was enhanced by integrating a dual-encoder architecture. The first encoder captures intra-entity risk using statistically significant financial indicators, legal records, and operational data. The second encoder models contagion dynamics using enterprise relation information derived from an enterprise knowledge graph. To collect the data, financial statements, including income, debt ratio, and liquidity indicators, along with credit scores and enterprise relationship information, were gathered from relevant financial databases and corporate records. The data was preprocessed by handling missing values and normalizing features. The SMO metaheuristic is employed to optimize attention weights, enhancing convergence and avoiding local minima. Experiments on a financial enterprise dataset demonstrate that SM-ISAGNN outperforms the baseline ISAGNN, achieving a higher path hit ratio (84.3% vs. 58.7%), a multi-hop detection rate (77.5% vs. 41.6%), and a lower false path prediction (7.9% vs. 21.3%). In 5-fold cross-validation, the model achieves an accuracy of 0.9667, a precision of 0.9658, a recall of 0.9667, and an F1-score of 0.9657. These results confirm SM-ISAGNN as a robust framework for early warning, risk visualization, and contagion path forecasting in financial enterprise networks.