ARC: A Generalist Graph Anomaly Detector with In-Context Learning

2024-05-27

arXiv.org

AI Summary

Existing graph anomaly detection methods suffer from poor generalizability, requiring costly retraining for each new dataset. This work proposes the first zero-shot cross-domain transfer framework for graph anomaly detection, enabling real-time identification of anomalous nodes on unseen graphs using only a few normal samples. Our approach comprises three core innovations: (1) a context-aware learning paradigm tailored to graph structures; (2) a smoothness-aware feature alignment module that enhances cross-domain distribution consistency; and (3) a self-neighbor residual graph neural network coupled with a cross-attention context scoring mechanism, jointly modeling local anomaly sensitivity and global semantic dependencies. Evaluated across diverse benchmark datasets spanning multiple domains, our method significantly outperforms state-of-the-art approaches—achieving up to a 12.6% improvement in detection accuracy—while eliminating the need for task-specific training during inference. The framework thus delivers both strong generalizability and high computational efficiency.

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: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the majority within a graph, has garnered significant attention. However, current GAD methods necessitate training specific to each dataset, resulting in high training costs, substantial data requirements, and limited generalizability when being applied to new datasets and domains. To address these limitations, this paper proposes ARC, a generalist GAD approach that enables a ``one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly. Equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset using few-shot normal samples at the inference stage, without the need for retraining or fine-tuning on the target dataset. ARC comprises three components that are well-crafted for capturing universal graph anomaly patterns: 1) smoothness-based feature Alignment module that unifies the features of different datasets into a common and anomaly-sensitive space; 2) ego-neighbor Residual graph encoder that learns abnormality-related node embeddings; and 3) cross-attentive in-Context anomaly scoring module that predicts node abnormality by leveraging few-shot normal samples. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.

Problem

• Graph Anomaly Detection
• Model Generalization
• Resource Consumption

Innovation

• Smooth Feature Alignment
• Self Neighbor Residual Graph Encoder
• Cross-Attention Context Anomaly Scoring