In the fraud detection, fraudsters frequently engage with numerous benign users to disguise their activities. Consequently, the fraud graph exhibits not only homogeneous connections between the fraudsters and the same labeled nodes, but also heterogeneous connections, where fraudsters interact with the legitimate nodes. Heterogeneous graph representation learning aims at extracting the structural and semantic information and embed it into the low-dimensional node representation. Recently, maximizing the mutual information between the local node embedding and the summary representation has achieved the promising results on node classification tasks. However, existing deep graph infomax methods still have the following limitations. Firstly, attribute information of nodes in the graph is not fully utilized for capturing the semantic relationships between nodes. Secondly, the local and global supervision signal are not simultaneously exploited for the node embedding learning. Thirdly, the multiplex heterogeneous relations among nodes are ignored. To address these issues, a heterogeneous graph representation learning model by mutual information estimation (MIE-HetGRL) is proposed in this paper to identify the fraudsters in the fraud review graph. Concretely, a high-order mutual information estimation is proposed to integrate the local and global mutual information as the supervision signal. Then we devise a semantic attention fusion module to aggregate the relation-aware node embeddings into a compact node representation. Finally, a joint contrastive learning is designed for facilitating the training and optimization of model. The experimental results show that our proposed model significantly outperforms state-of-the-art baselines for fraud detection.

中文翻译：

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通过互信息估计进行异构图表示学习，用于欺诈检测


在欺诈检测中，欺诈者经常与众多良性用户互动以掩盖他们的活动。因此，欺诈图不仅展示了欺诈者与相同标记节点之间的同构连接，还展示了欺诈者与合法节点交互的异构连接。异构图表示学习旨在提取结构和语义信息，并将其嵌入到低维节点表示中。最近，最大化局部节点嵌入和摘要表示之间的互信息在节点分类任务上取得了可喜的结果。但是，现有的深度图 infomax 方法仍然存在以下限制。首先，图中节点的属性信息没有被充分利用来捕获节点之间的语义关系;其次，局部和全局监督信号没有同时用于节点嵌入学习。第三，忽略节点之间的多路复用异构关系。针对这些问题，该文提出了一种基于互信息估计的异构图表示学习模型（MIE-HetGRL），用于识别欺诈审查图中的欺诈者。具体来说，提出了一种高阶互信息估计，将局部和全局互信息整合为监督信号。然后，我们设计一个语义注意力融合模块，将关系感知节点嵌入聚合成一个紧凑的节点表示。最后，设计了联合对比学习，以促进模型的训练和优化。实验结果表明，我们提出的模型明显优于最先进的欺诈检测基线。