AnomalyGFM is a graph foundation model designed for zero- and few-shot anomaly detection in graphs. It learns graph‑agnostic normal and abnormal prototypes by aligning data‑independent class representations with node representation residuals, enabling consistent abnormality measurement across diverse graph datasets. Experiments on 11 real‑world GAD datasets show that AnomalyGFM outperforms state‑of‑the‑art methods in both zero‑shot and few‑shot settings.
By Hezhe Qiao, Chaoxi Niu, Ling Chen, Guansong Pang
arXiv:2510. 02014v3 Announce Type: replace Abstract: Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications.
By Guolei Zeng, Hezhe Qiao, Guoguo Ai, Jinsong Guo, Guansong Pang
The paper investigates whether synthetic data can replace real-world training data for generalist graph anomaly detection (GAD). It introduces AG-FORGE, a framework that automatically generates anomalous graphs, and shows that synthetic data can match real-world performance but does not surpass it due to method limitations. To overcome this, the authors propose TS-GGAD, a model that jointly learns topological and semantic anomaly cues with a curriculum learning strategy, achieving superior results on 14 real-world datasets when trained on AG-FORGE-generated data.
By Yujing Liu, Yixin Liu, Yue Tan, Xiaofeng Cao, Alan Wee-Chung Liew, Heng Tao Shen, Shirui Pan
The paper introduces Jev-GAD, a training‑free generalist graph anomaly detection framework that combines complementary structural, attribute, and neighborhood evidence with a single frozen pretrained language model for anomaly assessment. It transforms heterogeneous statistics into graph‑relative percentiles and uses a semantic Graph‑to‑State (G2S) transformation to feed evidence into a non‑generative decision readout, fusing the resulting scores with four statistical measures. Experiments on seven benchmark datasets show Jev‑GAD achieving high AUROC (89.18%) and AUPRC (52.40%) scores, demonstrating the effectiveness of pretrained decision readout and semantic evidence grounding.
By Xinyu Zhao, Qingyun Sun, Ziwei Zhang, Tianchen Zhu, Daqing Li, Jianxin Li
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:2602. 20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.
By Yuxing Tian, Yiyan Qi, Fengran Mo, Weixu Zhang, Jian Guo, Jian-Yun Nie
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. 12673v1 Announce Type: cross Abstract: Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data.
By Phan Nguyen, Dat Cao, Hien Chu, Khue Hoang
arXiv:2609.38424v1 Announce Type: new
Abstract: Node-level graph anomaly detection (GAD) identifies nodes whose attributes and interactions deviate from dominant graph regularities. Existing GAD mode...
By Fred Xu, Thomas Markovich, Florence Regol, Yizhou Sun
arXiv:2608. 10699v1 Announce Type: cross Abstract: Text-Attributed Graphs (TAGs), endowed with abundant textual content along with topological structures, have emerged as a versatile backbone for real-world anomaly detection spanning large language model security, social network moderation, and cyber threat identification.
By Ziyan Wang, Liwen Wu, Cheng Xie, Song Gao, Zhenli He, Xin Jin
The paper introduces a deep positive‑unlabeled anomaly detection framework that combines positive‑unlabeled learning with deep models such as autoencoders and deep support vector data descriptions. It addresses the issue of contaminated unlabeled data by approximating anomaly scores for normal data using both unlabeled and labeled anomaly samples, allowing training without labeled normal data. The authors provide a theoretical generalization error bound and demonstrate improved detection performance over existing methods on several datasets.
By Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuuki Yamanaka