解决图数据属性缺失与欺诈样本稀少问题，提升欺诈检测效果。
A Novel Graph Fraud Detector via Grouped Attribute Completion and Confidence-Aware Contrastive Learning
- 分组聚合邻居结构完成节点属性，恢复完整特征
- 用高置信度伪欺诈节点增强少数类，提升模型区分能力
- 适合真实场景中标签稀疏的欺诈检测任务
图欺诈检测在保障现代数字生态系统安全与完整性方面具有关键作用。图神经网络（GNN）常用于图欺诈检测，但现有方法受节点属性不完整和图中类别极度不平衡的影响，性能受限。为此，本文提出一种名为GFD-GC的新框架，通过模仿异构邻域结构实现分组聚合，捕捉细粒度图上下文模式，从而获得信息丰富的完整节点特征。进一步引入置信度感知的监督对比学习策略，利用高置信度伪欺诈节点扩充稀缺的标注欺诈样本，增强欺诈表示的紧凑性及其与非欺诈节点的可分性。大量实验表明，所提GFD-GC在图欺诈检测任务上优于当前最优基线，为真实欺诈场景提供了有效解决方案。
原文摘要 · Abstract (English)
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 by incomplete node attributes and extreme class imbalance within graphs. To mitigate these limitations, this paper proposes a novel framework for Graph Fraud Detection with Grouped attribute completion and Confidence-aware Contrastive learning, named GFD-GC. Specifically, it first imitates heterogeneous neighborhood structures to implement group-wise aggregation, which obtains informative complete node features by capturing fine-grained graph contextual patterns. Further, it introduces a confidence-aware supervised contrastive learning strategy to augment scarce labeled fraud nodes with high confidence pseudo-fraud nodes, which enhances the compactness of fraud representations and their separability from non-fraud nodes. Extensive experiments demonstrate the superiority of the proposed GFD-GC over state-of-the-art baselines on the graph fraud detection task, thereby providing an effective solution for real-world fraud scenarios.
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