Graph Neural Networks (GNNs) have become the predominant approach for graph fraud detection due to their intrinsic capability to handle graph-structured data and effectively capture complex relational patterns in fraudulent behaviors. However, existing GNN-based graph fraud detection models face limitations: homophily-based models struggle with handling heterogeneous relationships in fraud graphs, while heterophily-based models typically model only a single attribute- or structural-space, leading to constrained detection performance.
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