Fraud detection on multi-relation graphs via imbalanced and interactive learning

Abstract

In recent years, the application of Graph Neural Networks (GNNs) in fraud detection has gained considerable attention. GNNs have demonstrated their efficacy in leveraging the abundant relational information inherent in graph-structured data for such tasks. However, despite the remarkable progress achieved thus far, several challenges still need to be addressed in the current GNN-based algorithms. On the one hand, GNNs exhibit limited performance when confronted with imbalanced label distribution between fraudsters and benign users. On the other hand, the embedding representations learned for nodes and their neighbors in the last layer are frequently processed in a simplistic manner, such as concatenation or averaging, which may compromise the performance of downstream tasks. To alleviate the above problems, we propose a novel imbalanced and interactive learning framework for fraud detection on multi-relation graphs (IMINF for short). Concretely, firstly, we design a novel neighbor sampler that can be trained using supervised contrastive learning. This sampler selects the most similar and consistent neighbors to update the target node's features, taking into account label information from multiple perspectives. Secondly, we introduce the learnable contrastive embedding and an improved relational aggregator to enhance the representation of the target node. Thirdly, we propose a new interactive learning module, which consists of explicit and implicit transformation layers, with the aim of capturing deep interactive relations between a target node and its neighbors. Additionally, we devise a feature mapping module prior to the relation aggregation step, transferring the raw input features to a latent feature subspace. Extensive experiments on two real-world fraud detection datasets demonstrate that the proposed model outperforms existing baselines across all evaluation metrics, especially for the Amazon fraud detection dataset.