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

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

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, e...

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Bibliographic Details
Main Authors: Shiyang Chen, Yang Liu, Qun Zhang, Zhouhang Shao, Zewei Wang
Format: Article
Language:English
Published: Wiley 2025-08-01
Series:Advanced Intelligent Systems
Subjects:
anomaly detection
blockchain
financial security
graph neural networks
temporal modeling
Online Access:https://doi.org/10.1002/aisy.202400898
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Summary: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.
ISSN:2640-4567

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