MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection

TL;DR

MetaGAD introduces a meta-learning framework that effectively leverages limited labeled anomalies and unlabeled data for improved few-shot graph anomaly detection across diverse real-world datasets.

Contribution

The paper proposes MetaGAD, a novel meta-learning approach that adapts self-supervised knowledge for few-shot graph anomaly detection, addressing overfitting and irregularity issues.

Findings

MetaGAD outperforms existing methods on six real-world datasets.

It effectively detects both synthetic and organic anomalies.

MetaGAD demonstrates strong generalization in few-shot settings.

Abstract

Graph anomaly detection has long been an important problem in various domains pertaining to information security such as financial fraud, social spam and network intrusion. The majority of existing methods are performed in an unsupervised manner, as labeled anomalies in a large scale are often too expensive to acquire. However, the identified anomalies may turn out to be uninteresting data instances due to the lack of prior knowledge. In real-world scenarios, it is often feasible to obtain limited labeled anomalies, which have great potential to advance graph anomaly detection. However, the work exploring limited labeled anomalies and a large amount of unlabeled nodes in graphs to detect anomalies is relatively limited. Therefore, in this paper, we study an important problem of few-shot graph anomaly detection. Nonetheless, it is challenging to fully leverage the information of few-shot anomalous nodes due to the irregularity of anomalies and the overfitting issue in the few-shot learning. To tackle the above challenges, we propose a novel meta-learning based framework, MetaGAD, that learns to adapt the knowledge from self-supervised learning to few-shot supervised learning for graph anomaly detection. In specific, we formulate the problem as a bi-level optimization, ensuring MetaGAD converging to minimizing the validation loss, thus enhancing the generalization capacity. The comprehensive experiments on six real-world datasets with synthetic anomalies and "organic" anomalies (available in the datasets) demonstrate the effectiveness of MetaGAD in detecting anomalies with few-shot anomalies. The code is available at https://github.com/XiongxiaoXu/MetaGAD.