发表机构
College of Computer and Information Science; Southwest University（计算机与信息科学学院; 西南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对图欺诈检测中现有GNN检测器受节点属性不完整和类不平衡限制的问题，提出GFD-GC框架，通过模仿邻域结构进行组内聚合补全节点特征，引入置信度感知对比学习增强欺诈表示，实验验证该框架优于现有基线。
AI 中文摘要
图欺诈检测对保障现代数字生态系统的安全与完整至关重要，常用图神经网络（GNNs）检测。但现有基于GNN的检测器受节点属性不完整和图内类极端不平衡影响。本文提出带分组属性补全和置信度感知对比学习的图欺诈检测框架GFD-GC。先模仿异构邻域结构进行组内聚合获取完整节点特征，再引入策略用高置信度伪欺诈节点增强稀缺标记欺诈节点，提升欺诈表示紧凑性及与非欺诈节点的可分性。实验证明其在图欺诈检测任务中优于现有基线，为实际欺诈场景提供有效方案。
英文摘要
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.
Comments9 pages,3 figures
