Supply Chain Disruption Risk Prediction Based on Hypergraph Representation and Dynamic Relational-Attentive

Abstract Traditional supply chain risk prediction methods, relying on historical data, expert judgment, scenario analysis, and simulation, exhibit limitations in handling sudden events and high uncertainty within complex systems. Typically leveraging historical semantic links in knowledge graphs, these methods forecast future relational facts among companies. To address these shortcomings, we construct a supply chain risk knowledge graph integrating multi-dimensional enterprise features. We propose a novel Hypergraph Dynamic Graph Attention Neural Network (HG-DRA) for disruption risk prediction. HG-DRA employs hypergraph representation learning and a dynamic relational attention mechanism. Experiments demonstrate that HG-DRA, by effectively integrating operational features, cluster characteristics, and complex heterogeneous graph relationships, outperforms existing machine learning and graph representation learning approaches in identifying supply chain disruption characteristics.