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Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection

arXiv – CS AI|Jiaqi Lv, Qingfeng Du, Yu Zhang, Yongqi Han, Sheng Li|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers propose MANDATE, a Multi-scale Neighborhood Awareness Transformer that improves graph fraud detection by addressing limitations of traditional graph neural networks. The system uses multi-scale positional encoding and different embedding strategies to better identify fraudulent behavior in financial networks and social media platforms.

Key Takeaways

→MANDATE addresses inherent biases in graph neural networks including homogeneity assumptions and limited global modeling ability.
→The system uses multi-scale positional encoding to capture information at various distances from central nodes.
→Different embedding strategies are employed for homophilic and heterophilic connections to better distinguish benign from fraudulent nodes.
→An embedding fusion strategy handles multi-relation graphs to reduce distribution bias from different relationship types.
→Experiments on three fraud detection datasets demonstrate MANDATE's superior performance over existing methods.

#fraud-detection #graph-neural-networks #transformers #financial-security #machine-learning #cybersecurity #research

Read Original →via arXiv – CS AI

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