  Graph-level anomaly detection has become a critical topic in diverse areas,
such as financial fraud detection and detecting anomalous activities in social
networks. While most research has focused on anomaly detection for visual data
such as images, where high detection accuracies have been obtained, existing
deep learning approaches for graphs currently show considerably worse
performance. This paper raises the bar on graph-level anomaly detection, i.e.,
the task of detecting abnormal graphs in a set of graphs. By drawing on ideas
from self-supervised learning and transformation learning, we present a new
deep learning approach that significantly improves existing deep one-class
approaches by fixing some of their known problems, including hypersphere
collapse and performance flip. Experiments on nine real-world data sets
involving nine techniques reveal that our method achieves an average
performance improvement of 11.8% AUC compared to the best existing approach.
