DDGAD introduces a novel approach to graph anomaly detection that focuses on the disagreement between node-wise and contextual estimates rather than on their combined state. By adapting the Adapt-Then-Combine framework, DDGAD generates separate node-wise and neighborhood-dependent estimates, accumulating their pre-consensus disagreement across iterations to identify anomalies. The method is theoretically grounded with graph-spectral and source-response analyses, and empirical results on six benchmarks demonstrate superior AUROC performance compared to existing techniques.
By Yuxin Yang, Limei Hu, Feng Chen
FoundAna is a GNN‑assisted foundation model designed for graph anomaly detection across diverse datasets. It combines a GNN component with a transformer encoder enhanced by four positional encodings to capture both local and global structure, using reconstruction errors as anomaly scores. Experiments on nine benchmark datasets from financial, social, and citation networks show that FoundAna consistently outperforms state‑of‑the‑art baselines.
By Suprim Nakarmi, Chahana Dahal, Yue Zhao, Junggab Son, Zuobin Xiong
arXiv:2602.06859v3 Announce Type: replace-cross
Abstract: Graph Anomaly Detection (GAD) aims to identify irregular patterns in graph data, and recent works have explored zero-shot generalist GAD to e...
By Xinyu Zhao, Qingyun Sun, Jiayi Luo, Xingcheng Fu, Jianxin Li
arXiv:2606. 00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes.
By Yilin Liu, Hongchao Zhang, Taylor T. Johnson, Ahmad F. Taha, Meiyi Ma
RINSE (Robust Iterative Normality Self-Estimation) is a gradient‑free framework for zero‑shot graph anomaly detection that keeps a source‑trained detector fixed while iteratively estimating target normality, calibrating representations, and assessing evidence reliability on unseen target graphs. It identifies a reliable subset of low‑residual target nodes to build a trimmed target‑aware normality model and fuses complementary anomaly evidence through reliability‑gated rank fusion and encoder ensembling. Across eight unseen target graphs, RINSE achieves the highest average AUPRC under two preprocessing protocols, with ablation and sensitivity analyses supporting its combined design.
By Taufikur Rahman Fuad, Md Abrar Jahin, Amir Hussain
arXiv:2605.26857v2 Announce Type: replace
Abstract: Driven by the pressing demand for graph anomaly detection (GAD) in high-stakes domains, the generalist GAD paradigm, which trains a single detector...
By Yiming Xu, Zihan Chen, Zhen Peng, Song Wang, Bin Shi, Bo Dong, Chao Shen