解决图异常检测中属性缺失问题，提升模型鲁棒性。
FGC-Comp: Adaptive Neighbor-Grouped Attribute Completion for Graph-based Anomaly Detection
- 按标签分组邻居，针对性补全属性
- 残差融合消息，提升聚合稳定性
- 轻量设计适合实际部署，适配各类检测器
基于图的异常检测模型近年广泛应用，通过聚合邻居信息识别可疑节点。然而，现有研究普遍忽视节点属性缺失或被恶意遮蔽的问题，这会破坏聚合稳定性与预测可靠性。为此，我们提出FGC-Comp：一种轻量、不依赖分类器、易于部署的属性补全模块，旨在不完整属性条件下增强邻居聚合效果。将每个节点的邻居划分为三类标签组，对已知标签组应用特定变换，节点条件门控处理未知项，通过残差连接融合信息，并以二分类目标端到端训练，提升聚合稳定性和预测可靠性。在两个真实世界欺诈数据集上的实验验证了方法的有效性，计算开销极低。
原文摘要 · Abstract (English)
Graph-based Anomaly Detection models have gained widespread adoption in recent years, identifying suspicious nodes by aggregating neighborhood information. However, most existing studies overlook the pervasive issues of missing and adversarially obscured node attributes, which can undermine aggregation stability and prediction reliability. To mitigate this, we propose FGC-Comp, a lightweight, classifier-agnostic, and deployment-friendly attribute completion module-designed to enhance neighborhood aggregation under incomplete attributes. We partition each node's neighbors into three label-based groups, apply group-specific transforms to the labeled groups while a node-conditioned gate handles unknowns, fuse messages via residual connections, and train end-to-end with a binary classification objective to improve aggregation stability and prediction reliability under missing attributes. Experiments on two real-world fraud datasets validate the effectiveness of the approach with negligible computational overhead.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。