MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection
Xiongxiao Xu Kaize Ding Canyu Chen Kai Shu
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.