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.

该论文提出一种基于自监督学习和转换学习的新型深度学习方法，显著改进了现有的深度单类方法，并在九个真实数据集上进行了实验，平均 AUC 性能提高了 11.8%。

提高图级异常检测的标准

Raising the Bar in Graph-level Anomaly Detection

With a good image understanding capability, can we manipulate the images high
level semantic representation? Such transformation operation can be used to
generate or retrieve similar images but with a desired modification (for
example changing beach background to street background); similar ability has
been demonstrated in zero shot learning, attribute composition and attribute
manipulation image search. In this work we show how one can learn
transformations with no training examples by learning them on another domain
and then transfer to the target domain. This is feasible if: first,
transformation training data is more accessible in the other domain and second,
both domains share similar semantics such that one can learn transformations in
a shared embedding space. We demonstrate this on an image retrieval task where
search query is an image, plus an additional transformation specification (for
example: search for images similar to this one but background is a street
instead of a beach). In one experiment, we transfer transformation from
synthesized 2D blobs image to 3D rendered image, and in the other, we transfer
from text domain to natural image domain.

本论文讨论了如何在不需要训练样本的情况下学习图像的语义表示，并展示了如何从另一个领域学习并应用变换操作到目标领域实现图像检索。