融合图增强与多教师动态蒸馏的区块链异常交易检测方法OA

To address the limitations of existing blockchain abnormal transaction detection methods in complex transac-tion structure modeling and data imbalance handling,this paper proposes a detection method called MGDD(multi-teacher GNN dynamic distillation)that integrates graph neural network teacher models,motif structural features,SMOTE sampling,and multi-student model knowledge distillation.This method first enhances node structural repres-entation using motif statistics,then constructs multiple teacher models by combining GCN,GAT,GIN,and Graph-SAGE graph neural networks.Through a dynamic distillation mechanism,it guides multi-layer perceptron(MLP)and random forest(RF)student models to learn high-order semantic features from graphs,and introduces SMOTE for over-sampling to alleviate the sample imbalance problem.Experimental results on the Elliptic dataset demonstrate that MG-DD outperforms existing typical methods in terms of accuracy,recall rate,and F1-score,with the RF student model achieving an accuracy rate of 99.23％under the guidance of distillation.Ablation and robustness experiments further verify the important role of motif features and knowledge distillation mechanisms in improving detection performance and model stability.In summary,MGDD provides an efficient and scalable modeling paradigm for blockchain abnor-mal transaction detection tasks,showing promising application prospects.