• Corpus ID: 246823762

Improved Aggregating and Accelerating Training Methods for Spatial Graph Neural Networks on Fraud Detection

@article{Zeng2022ImprovedAA,
  title={Improved Aggregating and Accelerating Training Methods for Spatial Graph Neural Networks on Fraud Detection},
  author={Yufan Zeng and Jiashan Tang},
  journal={ArXiv},
  year={2022},
  volume={abs/2202.06580}
}
Graph neural networks (GNNs) have been widely applied to numerous fields. A recent work which combines layered structure and residual connection proposes an improved deep architecture to extend CAmouflage-REsistant GNN (CARE-GNN) to deep models named as Residual Layered CARE-GNN (RLC-GNN), which forms self-correcting and incremental learning mechanism, and achieves significant performance improvements on fraud detection task. However, we spot three issues of RLC-GNN, which are the usage of… 
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