Graph Anomaly Detection with Domain-Agnostic Pre-Training and Few-Shot Adaptation

Graph anomaly detection attracts considerable interest across a variety of application domains, including fraud detection within social networks, identifying money laundering activities in transaction graphs, etc. The advent of Graph Neural Networks (GNNs) has enhanced existing deep learning methods to capture the anomaly patterns in the latent space and achieve satisfactory results. However, the performance deficiency of current GNNs is primarily caused by the scarce labeled anomalies in a specific real-world application. Unlike the total neglect of valuable labeled anomalies in unsupervised approaches or the potential overfitting in supervised approaches, we proposed a few-shot-oriented framework GUDI in this paper. GUDI is the first work to incorporate a self-supervised pre-training approach to capture general graph patterns across domains and design a classifier with few-shot learning to model the labeled data within a specific domain. The proposed guided diffusion mechanism synthesizes the outcomes of the pre-trained model and the domain-specific classifier in the inference phase, which negates the need for fine-tuning the large pre-trained model, thereby facilitating efficient domain adaptation. GUDI retains the ability to uncover unknown anomalies through unsupervised pre-training while also possessing the ability to identify known anomaly natterns from few-shot labels.