ML Anthology

DR-GGAD: Dual Residual Centering for Mitigating Anomaly Non‑Discriminativity in Generalist Graph Anomaly Detection

Changlong Fu, Zhenli He, Xiong Zhang, Cheng Xie, Xin Jin, Yun Yang
ICLR 2026
/iclr/2026/fu2026iclr-drggad/

Abstract

Generalist Graph Anomaly Detection (GGAD) seeks a unified representation learning model to detect anomalies in unseen graphs, but cross-domain transfer often entangles the learned anomalous and normal representations. We formalize this degradation as Anomaly non-Discriminativity (AnD) and define a normalized score to quantify it. We present DR-GGAD, which avoids direct comparison between anomalous and normal nodes via two residual modules: 1) a multi-scale Hyper Residual (HR) Center measuring node-to-center distances, yielding a compact normal residual structure with margin-pushed anomalies; 2) an Affinity-Residual (AR) module enforcing local residual directional consistency to recover structural separability. With frozen parameters (no target fine-tuning), DR-GGAD fuses both signals into a unified score. On 8 benchmark target graphs, it achieves new SOTA: mean AUROC +5.14% over the best prior GGAD, with large gains on high-AnD datasets (ACM +9.96%, Amazon +7.48%) and strong AUPRC boosts (Amazon +17.12%, CiteSeer +17.77%). Ablations confirm complementary roles of the two modules. DR-GGAD thus establishes AnD as a measurable bottleneck and delivers robust cross-domain anomaly detection.

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Cite

Text

Fu et al. "DR-GGAD: Dual Residual Centering for Mitigating Anomaly Non‑Discriminativity in Generalist Graph Anomaly Detection." International Conference on Learning Representations, 2026.

Markdown

[Fu et al. "DR-GGAD: Dual Residual Centering for Mitigating Anomaly Non‑Discriminativity in Generalist Graph Anomaly Detection." International Conference on Learning Representations, 2026.](https://mlanthology.org/iclr/2026/fu2026iclr-drggad/)

BibTeX

@inproceedings{fu2026iclr-drggad,
  title     = {{DR-GGAD: Dual Residual Centering for Mitigating Anomaly Non‑Discriminativity in Generalist Graph Anomaly Detection}},
  author    = {Fu, Changlong and He, Zhenli and Zhang, Xiong and Xie, Cheng and Jin, Xin and Yang, Yun},
  booktitle = {International Conference on Learning Representations},
  year      = {2026},
  url       = {https://mlanthology.org/iclr/2026/fu2026iclr-drggad/}
}