Graph anomaly detection has long been an important problem in various domains pertaining to information security such as financial fraud, social spam, network intrusion, etc. 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 data noises or uninteresting data instances due to the lack of prior knowledge on the anomalies. In realistic 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 rather limited. Therefore, in this paper, we study a novel problem of few-shot graph anomaly detection. We propose a new framework MetaGAD to learn to meta-transfer the knowledge between unlabeled and labeled nodes for graph anomaly detection. Experimental results on six real-world datasets with synthetic anomalies and "organic" anomalies (available in the dataset) demonstrate the effectiveness of the proposed approach in detecting anomalies with limited labeled anomalies.

翻译：图异常检测长期以来是金融欺诈、社交垃圾信息、网络入侵等涉及信息安全的多个领域中的重要问题。现有方法大多以无监督方式执行，因为大规模标注的异常样本通常获取成本过高。然而，由于缺乏对异常的先验知识，识别出的异常可能实际上是数据噪声或非感兴趣的数据实例。在实际场景中，获取有限的标注异常样本往往是可行的，这些样本在推进图异常检测方面具有巨大潜力。然而，探索如何利用图中有限的标注异常样本和大量未标注节点进行异常检测的研究仍相当有限。因此，本文研究了一个新问题——小样本图异常检测。我们提出了一种名为MetaGAD的新框架，通过学习将未标注节点与标注节点之间的知识进行元迁移，从而实现图异常检测。在六个真实世界数据集上进行的实验（包括合成异常和数据集中原有的"有机"异常）结果表明，所提方法在仅使用有限标注异常样本的情况下能够有效检测异常。