Heterogeneous Graph Structure Optimization with Dual-View Contrastive Learning for Fraud Detection

Fraud detection on multi-relational graphs is challenging because real-world fraud-related data often contains heterogeneous relations, noisy structures, and imbalanced labels. Existing GNN-based methods usually rely on predefined graph structures, but real-world financial graphs often contain noisy, redundant, or missing relations, which undermine neighborhood aggregation and message passing. In addition, single-view learning is insufficient to capture both local structural patterns and high-order semantic dependencies, limiting performance in complex fraud scenarios. To address this issue, we propose HGSO-DVCL, a heterogeneous graph structure optimization framework with dual-view contrastive learning. The framework performs type-aware structure optimization for each relation subgraph and integrates optimized graphs with the original structure through channel attention. A dual-view encoder then learns complementary representations from the network schema view and the meta-path view, while contrastive learning promotes consistency and complementarity between the two perspectives. An end-to-end objective jointly optimizes fraud classification, structure regularization, and contrastive alignment. Experiments on public multi-relational fraud detection benchmarks show that HGSO-DVCL achieves strong and competitive performance, while ablation and sensitivity studies support the effectiveness and stability of the proposed framework under the evaluated benchmark settings.