Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection
This work is significant for researchers and practitioners in the field of graph-based fraud detection, particularly in domains such as financial networks and social media, as it provides an improved method for identifying fraudulent behavior.
The authors tackled the problem of graph fraud detection and proposed a new model, MANDATE, which achieved superior results on three fraud detection datasets. The model addresses the limitations of existing graph neural network-based methods by incorporating multi-scale positional encoding and embedding strategies.
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.