Graph Neural Network has been proved to be effective for fraud detection for its capability to encode node interaction and aggregate features in a holistic view. Recently, Transformer network with great sequence encoding ability, has also outperformed other GNN-based methods in literatures. However, both GNN-based and Transformer-based networks only encode one perspective of the whole graph, while GNN encodes global features and Transformer network encodes local ones. Furthermore, previous works ignored encoding global interaction features of the heterogeneous graph with separate networks, thus leading to suboptimal performance. In this work, we present a novel framework called Relation-Aware GNN with transFormer (RAGFormer) which simultaneously embeds local and global features into a target node. The simple yet effective network applies a modified GAGA module where each transformer layer is followed by a cross-relation aggregation layer, to encode local embeddings and node interactions across different relations. Apart from the Transformer-based network, we further introduce a Relation-Aware GNN module to learn global embeddings, which is later merged into the local embeddings by an attention fusion module and a skip connection. Extensive experiments on two popular public datasets and an industrial dataset demonstrate that RAGFormer achieves the state-of-the-art performance. Substantial analysis experiments validate the effectiveness of each submodule of RAGFormer and its high efficiency in utilizing small-scale data and low hyper-parameter sensitivity.

翻译：图神经网络因其能够从整体视角编码节点交互和聚合特征而被证明对欺诈检测有效。近年来，具备强大序列编码能力的Transformer网络在文献中也已超越其他基于GNN的方法。然而，基于GNN和基于Transformer的网络仅编码了整个图的一个视角，其中GNN编码全局特征，而Transformer网络编码局部特征。此外，先前的工作忽略了使用独立网络对异构图全局交互特征的编码，从而导致性能次优。在这项工作中，我们提出了一个名为关系感知图神经网络与Transformer（RAGFormer）的新框架，该框架能够同时将局部和全局特征嵌入目标节点。这种简单而有效的网络采用了改进的GAGA模块，其中每个Transformer层之后都紧跟一个跨关系聚合层，以编码跨不同关系的局部嵌入和节点交互。除了基于Transformer的网络，我们进一步引入了关系感知GNN模块来学习全局嵌入，随后通过注意力融合模块和跳跃连接将其合并到局部嵌入中。在两个流行的公共数据集和一个工业数据集上进行的大量实验表明，RAGFormer达到了最先进的性能。大量分析实验验证了RAGFormer每个子模块的有效性，以及其在小规模数据利用上的高效性和低超参数敏感性。