发表机构
Nanyang Technological University; Lazada Inc.; Alibaba Group（南洋理工大学; 来扎达公司; 阿里巴巴集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对电商优惠券滥用检测中欺诈模式演变引发的分布偏移问题，提出CATeye框架，通过AIS与EIS选择不变属性和边，构建多视图优化，在Lazada数据集及公开基准上较9种基线最高提升平均F1分数8.61%。
AI 中文摘要
优惠券滥用是电子商务领域的重大挑战，恶意用户利用促销优惠券牟利。遗憾的是，欺诈模式随时间和地区快速演变，引发分布偏移，导致现有检测模型性能下降，除非频繁重新训练。为解决该问题，本文提出耦合属性-拓扑不变性学习框架（CATeye）。核心挑战源于耦合属性-拓扑偏移：由属性邻近性构建的边会使环境驱动的属性偏移诱导拓扑偏移，进而通过GNN消息传递放大变体信号。CATeye借助两个可学习选择器应对此类耦合偏移：首先，属性不变性选择器（AIS）学习节点自适应掩码以滤除非不变属性；随后，在保留的不变属性条件下，边不变性选择器（EIS）采样不变子图并分离非不变边。利用得到的不变与非不变组件，CATeye构建多个视图并应用视图特定目标，以强调领域不变表示同时抑制领域特定变化。在东南亚主要电商平台Lazada的专有数据集及公开基准上开展的实验表明，CATeye始终优于9种强大的领域泛化和图异常检测基线，较最强基线的平均F1分数提升最高达8.61%。源代码可在该URL公开获取。
英文摘要
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.
Comments8 pages, 3 figures, Accepted by CIKM 2026 Applied Research Track
