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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1002/aisy.202400898

Similar Items