Feature and structural uncertainty modeling for risk identification in supply chain networks

Supply chain networks are susceptible to various risks, and effectively identifying them is crucial for economic stability. Current risk identification methods, however, often fail to account for the intrinsic uncertainties in both the financial data (node features) and the complex web of business relationships (network structure). To address this, we propose the Conditional Hierarchical Variational Graph Autoencoder (CTVGAE), a novel framework that employs a variational inference framework to capture the inherent randomness in both a company’s financial data and its supply chain connections. By integrating a classifier with a conditional learning strategy, our model enhances its discriminative power to more reliably identify risky entities. Extensive experiments on a real-world supply chain dataset demonstrate that CTVGAE significantly outperforms nine baseline models, achieving an 8.79% increase in accuracy and a 5.23% gain in F1-score. These findings highlight the critical importance of modeling uncertainty for improving financial risk identification in complex networks and offer a powerful tool for practical applications.