Graph Neural Networks for Business Relationship Mining: Applications and Performance Analysis

Business ecosystems, such as supply-chain networks, financial transaction systems, and e-commerce platforms, exhibit complex relational structures that challenge traditional machine-learning models. Although graph neural networks (GNNs) have shown promise in capturing such dependencies, existing studies often focus on single domains, rely on static graphs, or lack systematic comparison across heterogeneous commercial settings. To address these gaps, this study proposes a unified analytical framework that integrates relational embeddedness theory, graph representation learning, and dynamic capability perspectives. Using three representative real-world scenarios, a retail procurement graph, an AML transaction network, and an e-commerce product affinity graph, we evaluate four GNN architectures (GCN, GraphSAGE, GAT, and Temporal-GNN) through link prediction, fraud detection, and recommendation tasks. The results show that attention-based models outperform others in heterogeneous supplier and transaction environments, temporal GNNs better capture evolving fraud patterns, and inductive architectures excel in high-turnover product graphs. These findings deepen theoretical understanding of relational learning in commercial systems and offer practical guidance for deploying GNN-based analytics in procurement risk assessment, financial compliance, and personalized recommendation services.