Research and Application of E-commerce Marketing Fraud Detection Method Based on Graph Neural Network

In recent years, graph-based E-commerce Fraud detection methods have received more and more attention, but there are still some problems. Firstly, fraudulent users only account for a small part of active users, and the imbalance between classes in the graph leads to model deviation and affect the effectiveness of E-commerce Fraud detection. At the same time, the existing methods have ignored the sparse connections between fraudulent entities. In addition, most existing methods rely on the cost-sensitive matrix given by the expert experience and cannot adapt to learn the optimal cost transformation according to the data distribution. In this paper, we propose a cost sensitive graph neural network based on adaptive neighbor sampling, termed as CNSGNN. Firstly, the model designs a label-aware adaptive neighbor selector, using reinforcement learning to train a suitable sampling threshold, sampling neighbors by label similarity, thus alleviating the imbalance in the graph initially. Then, similarity scores are used to enhance the connection between fraudulent entities and help the sharing and transmission of characteristic information of fraudulent nodes. After that, GNN is used for information aggregation, and the cost-sensitive matrix optimizer is used to learn the optimal cost transformation to improve the classification effect. The experimental results show that the proposed model is superior to the current advanced baseline methods.