English(EN) A Novel Graph Fraud Detector via Grouped Attribute Completion and Confidence-Aware Contrastive Learning

新的GFD-GC框架通过GNNs增强图欺诈检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-13 05:35

研究人员引入了一个名为GFD-GC的新框架，以利用图神经网络（GNNs）改进图欺诈检测。该方法解决了节点属性不完整和数据集不平衡带来的挑战。GFD-GC利用分组聚合来推导全面的节点特征，并采用一种置信度感知对比学习策略，通过高置信度的伪欺诈节点来增强有限的欺诈数据。实验表明，GFD-GC在检测图欺诈方面优于现有方法。 AI

影响这项研究通过提高GNN在不完整和不平衡图数据上的性能，为增强数字生态系统中的欺诈检测提供了一种新颖的方法。

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-13 05:35

一种新颖的图欺诈检测器，通过分组属性补全和置信度感知对比学习实现

Graph fraud detection plays a pivotal role in safeguarding the security and integrity of modern digital ecosystems. Graph Neural Networks (GNNs) are commonly adopted for graph fraud detection. However, the practical performance of existing GNN-based detectors is severely hindered…

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一种新颖的图欺诈检测器，通过分组属性补全和置信度感知对比学习实现

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