An edge feature aware heterogeneous graph neural network model to support tax evasion detection

作者：

Highlights：

• Graph neural network will help improve the tax evasion detection performance.

• Comprehensive features could be extracted by metapaths and hierarchical attention.

• Our method has been developed and applied in cooperation with Chinese Revenue Agency.

• Experiments on real-world dataset shows our method outperforms over 10+ baselines.

摘要

•Graph neural network will help improve the tax evasion detection performance.•Comprehensive features could be extracted by metapaths and hierarchical attention.•Our method has been developed and applied in cooperation with Chinese Revenue Agency.•Experiments on real-world dataset shows our method outperforms over 10+ baselines.

论文关键词：Tax evasion detection,Heterogeneous graph,Classification,Anomaly detection

论文评审过程：Received 16 April 2022, Revised 16 September 2022, Accepted 22 September 2022, Available online 5 October 2022, Version of Record 20 October 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118903