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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-08-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400898 |
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| _version_ | 1849230072677924864 |
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| author | Shiyang Chen Yang Liu Qun Zhang Zhouhang Shao Zewei Wang |
| author_facet | Shiyang Chen Yang Liu Qun Zhang Zhouhang Shao Zewei Wang |
| author_sort | Shiyang Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2cf4c0ea5d0c4751a60966ba82ef9e14 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-2cf4c0ea5d0c4751a60966ba82ef9e142025-08-21T11:05:47ZengWileyAdvanced Intelligent Systems2640-45672025-08-0178n/an/a10.1002/aisy.202400898Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain TransactionsShiyang Chen0Yang Liu1Qun Zhang2Zhouhang Shao3Zewei Wang4College of Engineering Texas A&M University College Station TX 77840 USADepartment of Computer Science Worcester Polytechnic Institute Worcester MA 01609 USADepartment of Statistics and Biostatistics California State University, East Bay Hayward CA 94542 USADepartment of Computer Science and Engineering University of California San Diego San Diego CA 92093 USADepartment of Information Engineering The Chinese University of Hong Kong Hong Kong ChinaThis 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.https://doi.org/10.1002/aisy.202400898anomaly detectionblockchainfinancial securitygraph neural networkstemporal modeling |
| spellingShingle | Shiyang Chen Yang Liu Qun Zhang Zhouhang Shao Zewei Wang Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions Advanced Intelligent Systems anomaly detection blockchain financial security graph neural networks temporal modeling |
| title | Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions |
| title_full | Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions |
| title_fullStr | Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions |
| title_full_unstemmed | Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions |
| title_short | Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions |
| title_sort | multi distance spatial temporal graph neural network for anomaly detection in blockchain transactions |
| topic | anomaly detection blockchain financial security graph neural networks temporal modeling |
| url | https://doi.org/10.1002/aisy.202400898 |
| work_keys_str_mv | AT shiyangchen multidistancespatialtemporalgraphneuralnetworkforanomalydetectioninblockchaintransactions AT yangliu multidistancespatialtemporalgraphneuralnetworkforanomalydetectioninblockchaintransactions AT qunzhang multidistancespatialtemporalgraphneuralnetworkforanomalydetectioninblockchaintransactions AT zhouhangshao multidistancespatialtemporalgraphneuralnetworkforanomalydetectioninblockchaintransactions AT zeweiwang multidistancespatialtemporalgraphneuralnetworkforanomalydetectioninblockchaintransactions |