论文标题
减轻将图神经网络应用于欺诈检测的不一致问题
Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection
论文作者
论文摘要
基于图的模型可以帮助在线检测可疑欺诈。由于图形神经网络〜（GNN）的发展，先前的研究工作提出了许多基于同质图或异质图的基于GNN的欺诈检测框架。这些工作遵循现有的GNN框架，通过汇总相邻信息来学习节点嵌入，这是基于邻居共享相似上下文，特征和关系的假设。但是，几乎没有研究不一致的问题，即上下文不一致，特征不一致和关系不一致。 In this paper, we introduce these inconsistencies and design a new GNN framework, $\mathsf{GraphConsis}$, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features, (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability, and （3）对于关系不一致，我们学习了与采样节点相关的关注权重。在四个数据集上的经验分析表明，不一致问题对于欺诈检测任务至关重要。广泛的实验证明了$ \ mathsf {graphConsis} $的有效性。我们还发布了一个基于GNN的欺诈检测工具箱，该工具箱具有SOTA模型的实现。该代码可在https://github.com/saf-graph/dgfraud上找到。
The graph-based model can help to detect suspicious fraud online. Owing to the development of Graph Neural Networks~(GNNs), prior research work has proposed many GNN-based fraud detection frameworks based on either homogeneous graphs or heterogeneous graphs. These work follow the existing GNN framework by aggregating the neighboring information to learn the node embedding, which lays on the assumption that the neighbors share similar context, features, and relations. However, the inconsistency problem is hardly investigated, i.e., the context inconsistency, feature inconsistency, and relation inconsistency. In this paper, we introduce these inconsistencies and design a new GNN framework, $\mathsf{GraphConsis}$, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features, (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability, and (3) for the relation inconsistency, we learn a relation attention weights associated with the sampled nodes. Empirical analysis on four datasets indicates the inconsistency problem is crucial in a fraud detection task. The extensive experiments prove the effectiveness of $\mathsf{GraphConsis}$. We also released a GNN-based fraud detection toolbox with implementations of SOTA models. The code is available at https://github.com/safe-graph/DGFraud.
