  Anomaly detection aims to distinguish abnormal instances that deviate
significantly from the majority of benign ones. As instances that appear in the
real world are naturally connected and can be represented with graphs, graph
neural networks become increasingly popular in tackling the anomaly detection
problem. Despite the promising results, research on anomaly detection has
almost exclusively focused on static graphs while the mining of anomalous
patterns from dynamic graphs is rarely studied but has significant application
value. In addition, anomaly detection is typically tackled from semi-supervised
perspectives due to the lack of sufficient labeled data. However, most proposed
methods are limited to merely exploiting labeled data, leaving a large number
of unlabeled samples unexplored. In this work, we present semi-supervised
anomaly detection (SAD), an end-to-end framework for anomaly detection on
dynamic graphs. By a combination of a time-equipped memory bank and a
pseudo-label contrastive learning module, SAD is able to fully exploit the
potential of large unlabeled samples and uncover underlying anomalies on
evolving graph streams. Extensive experiments on four real-world datasets
demonstrate that SAD efficiently discovers anomalies from dynamic graphs and
outperforms existing advanced methods even when provided with only little
labeled data.
