Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions
Shiyang Chen, Yang Liu, Qun Zhang, Z. Shao, Zewei Wang
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.