Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts

2024-10-18

arXiv.org

AI Summary

Existing graph anomaly detection (GAD) methods rely on single-dataset training and suffer from poor zero-shot generalization across graphs. This paper proposes the first zero-shot universal GAD framework, enabling cross-dataset transfer without retraining or fine-tuning on target graphs. Methodologically, it introduces: (1) a novel zero-shot universal detection paradigm; (2) a unified anomaly score grounded in normalized node attribute predictability; and (3) a transferable unified neighborhood prompting mechanism that integrates coordinate-level projection-space normalization with implicit attribute prediction modeling. Evaluated on multiple real-world graph datasets, the method achieves significant performance gains over diverse baselines under the universal detection setting, while maintaining strong competitiveness under conventional single-dataset training. The framework thus bridges the gap between domain-specific GAD and practical deployment across heterogeneous graph domains.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. Existing GAD methods, whether supervised or unsupervised, are one-model-for-one-dataset approaches, i.e., training a separate model for each graph dataset. This limits their applicability in real-world scenarios where training on the target graph data is not possible due to issues like data privacy. To overcome this limitation, we propose a novel zero-shot generalist GAD approach UNPrompt that trains a one-for-all detection model, requiring the training of one GAD model on a single graph dataset and then effectively generalizing to detect anomalies in other graph datasets without any retraining or fine-tuning. The key insight in UNPrompt is that i) the predictability of latent node attributes can serve as a generalized anomaly measure and ii) highly generalized normal and abnormal graph patterns can be learned via latent node attribute prediction in a properly normalized node attribute space. UNPrompt achieves generalist GAD through two main modules: one module aligns the dimensionality and semantics of node attributes across different graphs via coordinate-wise normalization in a projected space, while another module learns generalized neighborhood prompts that support the use of latent node attribute predictability as an anomaly score across different datasets. Extensive experiments on real-world GAD datasets show that UNPrompt significantly outperforms diverse competing methods under the generalist GAD setting, and it also has strong superiority under the one-model-for-one-dataset setting.

Problem

• Develops zero-shot generalist graph anomaly detection
• Eliminates need for dataset-specific model training
• Uses unified neighborhood prompts for cross-dataset generalization

Innovation

• Zero-shot generalist GAD approach UNPrompt
• Latent node attribute predictability as anomaly measure
• Generalized neighborhood prompts for cross-dataset anomaly detection