CATeye: Coupled Attribute-Topology Invariance Learning for Voucher Abuse Detection
cs.LG
Submitted: 2026-09-01
Updated: 2026-09-01
Comments: 8 pages, 3 figures, Accepted by CIKM 2026 Applied Research Track
Code: https://github.com/Tian0426/CATeye
License: http://creativecommons.org/licenses/by/4.0/
The gist: Voucher abuse poses a major challenge in e-commerce, where malicious users exploit promotional vouchers for profit.
Terminology
Abstract
Voucher abuse poses a major challenge in e-commerce, where malicious users exploit promotional vouchers for profit. Unfortunately, fraud patterns evolve rapidly over time and across regions, causing distribution shifts that degrade existing detection models unless retrained frequently. To tackle this, we propose the Coupled Attribute-Topology Invariance Learning framework (CATeye). The key challenge arises from coupled attribute-topology shift, where edges built from attribute proximity cause environment-driven attribute shift to induce shifted topology, thereby amplifying variant signals through GNN message passing. CATeye sees through such coupled shifts with two learnable selectors. First, an Attribute Invariance Selector (AIS) learns node-adaptive masks to filter out non-invariant attributes. Then, conditioned on retained invariant attributes, an Edge Invariance Selector (EIS) samples an invariant subgraph and isolates non-invariant edges. Using the resulting invariant and non-invariant components, CATeye constructs multiple views and applies view-specific objectives to emphasize domain-invariant representations while suppressing domain-specific variations. Experiments on both a proprietary dataset from Lazada, a major Southeast Asian e-commerce platform, and a public benchmark show that CATeye consistently outperforms nine strong domain generalization and graph anomaly detection baselines, achieving up to an 8.61% improvement in average F1 score over the strongest baseline. Source code is publicly available at https://github.com/Tian0426/CATeye.
Sources
- Invariant Risk Minimization
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- Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
- Handling Distribution Shifts on Graphs: An Invariance Perspective
- Discovering Invariant Rationales for Graph Neural Networks
- A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
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