Graph fraud detection (GFD) is crucial for identifying fraudulent behavior within graphs, benefiting various domains such as financial networks and social media. Existing methods based on graph neural networks (GNNs) have succeeded considerably due to their effective expressive capacity for graph-structured data. However, the inherent inductive bias of GNNs, including the homogeneity assumption and the limited global modeling ability, hinder the effectiveness of these models. To address these challenges, we propose Multi-scale Neighborhood Awareness Transformer (MANDATE), which alleviates the inherent inductive bias of GNNs. Specifically, we design a multi-scale positional encoding strategy to encode the positional information of various distances from the central node. By incorporating it with the self-attention mechanism, the global modeling ability can be enhanced significantly. Meanwhile, we design different embedding strategies for homophilic and heterophilic connections. This mitigates the homophily distribution differences between benign and fraudulent nodes. Moreover, an embedding fusion strategy is designed for multi-relation graphs, which alleviates the distribution bias caused by different relationships. Experiments on three fraud detection datasets demonstrate the superiority of MANDATE.

翻译：图欺诈检测（GFD）对于识别图中的欺诈行为至关重要，有助于金融网络和社交媒体等多个领域。基于图神经网络（GNNs）的现有方法因其对图结构数据的有效表达能力而取得了显著成功。然而，GNNs固有的归纳偏置，包括同质性假设和有限的全局建模能力，阻碍了这些模型的有效性。为解决这些挑战，我们提出了多尺度邻域感知Transformer（MANDATE），以缓解GNNs的固有归纳偏置。具体而言，我们设计了一种多尺度位置编码策略，用于编码中心节点不同距离的位置信息。通过将其与自注意力机制结合，可以显著增强全局建模能力。同时，我们为同质性和异质性连接设计了不同的嵌入策略。这缓解了良性节点与欺诈节点之间的同质性分布差异。此外，针对多关系图设计了一种嵌入融合策略，以减轻由不同关系引起的分布偏置。在三个欺诈检测数据集上的实验证明了MANDATE的优越性。