[2603.03106] Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection

Computer Science > Machine Learning arXiv:2603.03106 (cs) [Submitted on 3 Mar 2026] Title:Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection Authors:Jiaqi Lv, Qingfeng Du, Yu Zhang, Yongqi Han, Sheng Li View a PDF of the paper titled Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection, by Jiaqi Lv and 4 other authors View PDF HTML (experimental) Abstract: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 nod...