Multi-Distance Spatial-Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions

Abstract

This article presents MDST-GNN, a multi-distance spatial-temporal graph neural network for blockchain anomaly detection. To address challenges in detecting fraudulent cryptocurrency transactions, MDST-GNN integrates a multi-distance graph convolutional architecture with adaptive temporal modeling, enabling capture of both local and global spatial dependencies while inferring patterns from anonymized temporal data. The model incorporates self-supervised learning to enhance generalization ability. Experiments on the Elliptic dataset demonstrate MDST-GNN's superior performance over state-of-the-art methods, achieving improvements of 1.5% in AUC-ROC and 2.9% in AUC-PR. The model's robustness to temporal granularity and effectiveness in identifying suspicious transactions underscore its practical value for blockchain forensics.