FGC-Comp: Adaptive Neighbor-Grouped Attribute Completion for Graph-based Anomaly Detection
This work addresses a specific issue in graph-based anomaly detection for fraud detection, but it is incremental as it builds on existing methods by adding a completion module.
The paper tackled the problem of missing and adversarially obscured node attributes in graph-based anomaly detection, which can undermine aggregation stability and prediction reliability, by proposing FGC-Comp, a lightweight attribute completion module that improved performance on two real-world fraud datasets with negligible computational overhead.
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.