LGM-GNN: A Local and Global Aware Memory-Based Graph Neural Network for Fraud Detection

Graphs have been widely adopted to accomplish fraud detection tasks because of their inherently favorable structure to capture the intricate features in many complicated scenarios, especially in some modern e-commerce situations that have various relation attributes like transactions. These works tend to utilize the direct aggregate information about neighbor nodes of the target node or the aggregation of the neighbor information after the conditional filter and mostly use local information but ignore global information. However, in some cases, local abnormal points are detected in the global view, so global information is very important for fraud detection. In this article, we propose a local and global aware memory-based graph neural network for fraud detection (LGM-GNN). It first obtains the preliminary node embedding through relation-aware embedding and then interactively aggregates the local and global memory network to fuse and utilize the local and global information. Finally, the node embeddings of different levels are aggregated through the hierarchical information aggregator. Extensive experiments show our proposed LGM-GNN outperforms other SOAT methods on two real-world fraud detection datasets.

Automated summary

Graphs are widely used in fraud detection tasks to capture complex features in scenarios with various relation attributes like transactions. However, existing methods mostly ignore global information, which is important in detecting local abnormal points. In this article, we propose a local and global aware memory-based graph neural network (LGM-GNN) that utilizes both local and global information through relation-aware embedding and interactive aggregation. Experimental results show that LGM-GNN outperforms other methods on real-world fraud detection datasets.