Graph Neural Networks (GNNs) have been widely applied to fraud detection
problems in recent years, revealing the suspiciousness of nodes by aggregating
their neighborhood information via different relations. However, few prior
works have noticed the camouflage behavior of fraudsters, which could hamper
the performance of GNN-based fraud detectors during the aggregation process. In
this paper, we introduce two types of camouflages based on recent empirical
studies, i.e., the feature camouflage and the relation camouflage. Existing
GNNs have not addressed these two camouflages, which results in their poor
performance in fraud detection problems. Alternatively, we propose a new model
named CAmouflage-REsistant GNN (CARE-GNN), to enhance the GNN aggregation
process with three unique modules against camouflages. Concretely, we first
devise a label-aware similarity measure to find informative neighboring nodes.
Then, we leverage reinforcement learning (RL) to find the optimal amounts of
neighbors to be selected. Finally, the selected neighbors across different
relations are aggregated together. Comprehensive experiments on two real-world
fraud datasets demonstrate the effectiveness of the RL algorithm. The proposed
CARE-GNN also outperforms state-of-the-art GNNs and GNN-based fraud detectors.
We integrate all GNN-based fraud detectors as an opensource toolbox:
this https URL The CARE-GNN code and datasets are
available at this https URL

本文提出了一种名为 CARE-GNN 的新型 Graph Neural Network 模型对付欺诈检测中恶意节点使用的特征伪装和关系伪装，CARE-GNN 包括三个模块，即标签感知型相似度衡量模块、强化学习模块和跨不同关系的选中邻居的聚合模块，实验表明，CARE-GNN 优于现有的 GNN 模型和 GNN-based 欺诈检测器。