← Back to all news
arXiv Machine Learning

One Pretrained Decision Model Is Enough for Training-Free Generalist Graph Anomaly Detection

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.

Related stories

arXiv AI
2d ago

AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection

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
benchmarks
More like this →
arXiv Machine Learning
Sep 17

FoundAna: A GNN-assisted Foundation Model for Graph Anomaly Detection

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
llmsbenchmarks
More like this →
arXiv Machine Learning
Oct 9

Is Real-World Training Data Necessary for Generalist Graph Anomaly Detection?

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
fine-tuningbenchmarks
More like this →
arXiv Machine Learning
Sep 7

GLASS: Graph-Language Alignment with Spherical Scoring for Transferable Graph-Level Anomaly Detection

GLASS is a graph‑level anomaly detection framework that aligns graph and language representations on a unit hypersphere to achieve cross‑domain transferability. It constructs a Graph Descriptor Prompt to encode local, global, and semantic graph properties, and uses a multi‑slice soft cosine objective to unify graph and text embeddings. Anomaly scoring is performed via spherical density estimation with von Mises‑Fisher kernels, enabling zero‑shot detection and few‑shot adaptation across twelve benchmarks and three meta‑domains, outperforming recent GLAD baselines.

By Xudong Wang, Chris Ding, Tongxin Li, Jicong Fan
ragmultimodalbenchmarkssafety
More like this →