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.

本文提出三个方法以解决 Residual Layered CARE-GNN 的三个问题：采用余弦距离进行相似度度量，结合邻节点和批次规范化的相似度量模块，以及中间信息补充。在 Yelp 和 Amazon 数据集上，采用这三种方法分别获得了 4.81％，6.62％，6.81％的提高。