RWK-GNN：基于特征增强与子核分解的非平衡图欺诈检测算法

Financial fraud poses a serious threat to the economic and social stability, making the development of effective fraud detection algorithms crucial for safeguarding the integrity of the financial system. Currently, various graph-based fraud detection algorithms have been applied in practical scenarios. These methods either classify based on the structural information of graphs or utilize graph convolutional neural networks to learn embedded representations of nodes for fraud detection. However, these approaches have relatively narrow perspectives and cannot comprehensively analyze fraud detection on imbalanced multi-relational graphs. To address these issues, this paper proposes a RWK-GNN (Random Walk feature enhancement and Kcore subkernel decomposition Graph Neural Network), which efficiently extracts topological information at both the node level and the global network level in imbalanced graphs with multiple relationships. It optimizes the propagation and aggregation of graph structural features from the perspective of community evolution through subkernel decomposition algorithm, ultimately achieving fraud detection and identification. To validate the performance of the RWK-GNN algorithm, this study employs commonly used public datasets for graph neural network fraud detection tasks in model training and testing. Experimental results demonstrate significant improvements of this method over other machine learning algorithms and graph neural network algorithms in terms of the same evaluation metrics. Compared to the CARE-GNN algorithm, the proposed method achieves a 17% increase in AUC value. Compared to the PC-GNN algorithm, the proposed method achieves an 8% increase in AUC value. Moreover, compared to the SIGN algorithm, the proposed method achieves a 7% increase in AUC value.