Graph Pre-Training Models Are Strong Anomaly Detectors

TL;DR

This paper demonstrates that graph pre-training models are highly effective for anomaly detection in graphs, especially with limited supervision, outperforming end-to-end models and detecting distant, under-represented anomalies.

Contribution

The study reveals the strong anomaly detection capabilities of graph pre-training models and uncovers their ability to detect distant, unlabeled anomalies beyond 2-hop neighborhoods.

Findings

Pre-training models outperform end-to-end models in GAD.

Pre-training enhances detection of distant, unlabeled anomalies.

Pre-training is effective for both node and graph-level anomaly detection.

Abstract

Graph Anomaly Detection (GAD) is a challenging and practical research topic where Graph Neural Networks (GNNs) have recently shown promising results. The effectiveness of existing GNNs in GAD has been mainly attributed to the simultaneous learning of node representations and the classifier in an end-to-end manner. Meanwhile, graph pre-training, the two-stage learning paradigm such as DGI and GraphMAE, has shown potential in leveraging unlabeled graph data to enhance downstream tasks, yet its impact on GAD remains under-explored. In this work, we show that graph pre-training models are strong graph anomaly detectors. Specifically, we demonstrate that pre-training is highly competitive, markedly outperforming the state-of-the-art end-to-end training models when faced with limited supervision. To understand this phenomenon, we further uncover pre-training enhances the detection of distant, under-represented, unlabeled anomalies that go beyond 2-hop neighborhoods of known anomalies, shedding light on its superior performance against end-to-end models. Moreover, we extend our examination to the potential of pre-training in graph-level anomaly detection. We envision this work to stimulate a re-evaluation of pre-training's role in GAD and offer valuable insights for future research.