MINT: Detecting Fraudulent Behaviors from Time-series Relational Data
Summary: MINT constructs a time-aware behavior graph per user (rows as action nodes) and learns hierarchical short/medium/long-term intentions via three temporal GCNs with a gated neighbor interaction to capture row-level effects and avoid over-smoothing. Its exponential receptive-field design uses far fewer GCN layers (no RNN), improving training efficiency and scalability on billion-scale e-commerce logs and outperforming 10 SOTA models with better interpretability. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Fei Xiao
- 2. Yuncheng Wu
- 3. Meihui Zhang
- 4. Gang Chen
- 5. Beng Chin Ooi
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