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

arXiv: https://arxiv.org/abs/2202.06580 · HTML: https://mlsanity.com/paper/2202.06580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Towards Deeper Graph Neural Networks](https://mlsanity.com/paper/2007.09296/) (2020)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [Training Graph Neural Networks with 1000 Layers](https://mlsanity.com/paper/2106.07476/) (2021)
- [TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition](https://mlsanity.com/paper/1703.10667/) (2017)
- [Learning Graph Normalization for Graph Neural Networks](https://mlsanity.com/paper/2009.11746/) (2020)

## Abstract (arXiv)

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 neighboring information reaching limitation, the training difficulty which is inherent problem to deep models and lack of comprehensive consideration about node features and external patterns. In this work, we propose three approaches to solve those three problems respectively. First, we suggest conducting similarity measure via cosine distance to take both local features and external patterns into consideration. Then, we combine the similarity measure module and the idea of adjacency-wise normalization with node-wise and batch-wise normalization and then propound partial neighborhood normalization methods to overcome the training difficulty while mitigating the impact of too much noise caused by high-density of graph. Finally, we put forward intermediate information supplement to solve the information limitation. Experiments are conducted on Yelp and Amazon datasets. And the results show that our proposed methods effectively solve the three problems. After applying the three methods, we achieve 4.81%, 6.62% and 6.81% improvements in the metrics of recall, AUC and Macro-F1 respectively on the Yelp dataset. And we obtain 1.65% and 0.29% improvements in recall and AUC respectively on the Amazon datasets.