English(EN) CATeye: Coupled Attribute-Topology Invariance Learning for Voucher Abuse Detection

新的CATeye框架应对不断演变的电子商务代金券欺诈

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一个名为CATeye的新框架，以应对电子商务中不断演变的代金券滥用问题。该方法解决了耦合属性-拓扑变化带来的挑战，在这种变化中，属性邻近性的改变会改变图的拓扑结构，从而放大了图神经网络中的检测错误。CATeye利用属性不变性选择器过滤不相关的属性，并利用边不变性选择器分离不变的子图，从而构建多个视图以实现鲁棒的域不变表示学习。在Lazada的专有数据集和公开基准上的实验表明，CATeye具有优越性，在平均F1分数上比现有方法提高了高达8.61%。 AI

影响这项研究为检测电子商务中不断演变的欺诈模式提供了一种新颖的方法，有望提高检测系统的准确性和鲁棒性。

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在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Tian Tian, Shuaicheng Niu, Hao Kuang, Yuanhang Hu, Dong Li, Zhiqi Shen · 2026-09-02 04:00

CATeye：用于凭证滥用检测的耦合属性-拓扑不变性学习

arXiv:2609.01425v1 Announce Type: new 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 …

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报道来源 [1]

CATeye：用于凭证滥用检测的耦合属性-拓扑不变性学习

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