TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detection

2026-03-10

AI Summary

This work addresses the challenge of cross-domain graph anomaly detection, where domain shift often leads to feature misalignment and severely limits model generalization—particularly due to the pronounced anomaly disassortativity (AD) phenomenon, wherein anomalous nodes exhibit distinct connectivity patterns compared to normal ones. The paper formally defines and quantifies AD for the first time and introduces a test-time adaptive graph foundation model that, after a single training phase, generalizes effectively across diverse real-world graph domains without retraining. By integrating graph neural networks, explicit modeling of anomaly disassortativity, and a universal detection architecture, the proposed method achieves state-of-the-art performance across 14 real-world datasets, significantly improving cross-domain anomaly detection accuracy.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

A significant number of anomalous nodes in the real world, such as fake news, noncompliant users, malicious transactions, and malicious posts, severely compromises the health of the graph data ecosystem and urgently requires effective identification and processing. With anomalies that span multiple data domains yet exhibit vast differences in features, cross-domain detection models face severe domain shift issues, which limit their generalizability across all domains. This study identifies and quantitatively analyzes a specific feature mismatch pattern exhibited by domain shift in graph anomaly detection, which we define as the \emph{Anomaly Disassortativity} issue ($\mathcal{AD}$). Based on the modeling of the issue $\mathcal{AD}$, we introduce a novel graph foundation model for anomaly detection. It achieves cross-domain generalization in different graphs, requiring only a single training phase to perform effectively across diverse domains. The experimental findings, based on fourteen diverse real-world graphs, confirm a breakthrough in the model's cross-domain adaptation, achieving a pioneering state-of-the-art (SOTA) level in terms of detection accuracy. In summary, the proposed theory of $\mathcal{AD}$ provides a novel theoretical perspective and a practical route for future research in generalist graph anomaly detection (GGAD). The code is available at https://anonymous.4open.science/r/Anonymization-TA-GGAD/.

Problem

• graph anomaly detection
• domain shift
• cross-domain generalization
• anomaly disassortativity
• generalist graph learning

Innovation

• Anomaly Disassortativity
• Graph Foundation Model
• Cross-domain Generalization
• Test-time Adaptation
• Generalist Graph Anomaly Detection