IA-GGAD: Zero-shot Generalist Graph Anomaly Detection via Invariant and Affinity Learning
Xiong Zhang, Zhenli He, Changlong Fu, Cheng Xie
Abstract
Generalist Graph Anomaly Detection (GGAD) extends traditional Graph Anomaly Detection (GAD) from one-for-one to one-for-all scenarios, posing significant challenges due to Feature Space Shift (FSS) and Graph Structure Shift (GSS). This paper first formalizes these challenges and proposes quantitative metrics to measure their severity. To tackle FSS, we develop an anomaly-driven graph invariant learning module that learns domain-invariant node representations. To address GSS, a novel structure-insensitive affinity learning module is introduced, capturing cross-domain structural correspondences via affinity-based features. Our unified framework, IA-GGAD, integrates these modules, enabling anomaly prediction on unseen graphs without target-domain retraining or fine-tuning. Extensive experiments on benchmark datasets from varied domains demonstrate IA-GGAD’s superior performance, significantly outperforming state-of-the-art methods (e.g., achieving up to +12.28\% AUROC over ARC on ACM). Ablation studies further confirm the effectiveness of each proposed module. The code is available at \url{https://github.com/kg-cc/IA-GGAD/}.
BibTeX
@inproceedings{
zhang2025iaggad,
title={{IA}-{GGAD}: Zero-shot Generalist Graph Anomaly Detection via Invariant and Affinity Learning},
author={Xiong Zhang and Zhenli He and Changlong Fu and Cheng Xie},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=Cggdvyt8ik}
}