Improving Generalizability of Graph Anomaly Detection Models via Data
  Augmentation
Shuang Zhou Xiao Huang Ninghao Liu Huachi Zhou Fu-Lai Chung Long-Kai Huang
Graph anomaly detection (GAD) is a vital task since even a few anomalies can
pose huge threats to benign users. Recent semi-supervised GAD methods, which
can effectively leverage the available labels as prior knowledge, have achieved
superior performances than unsupervised methods. In practice, people usually
need to identify anomalies on new (sub)graphs to secure their business, but
they may lack labels to train an effective detection model. One natural idea is
to directly adopt a trained GAD model to the new (sub)graph for testing.
However, we find that existing semi-supervised GAD methods suffer from poor
generalization issue, i.e., well-trained models could not perform well on an
unseen area (i.e., not accessible in training) of the same graph. It may cause
great troubles. In this paper, we base on the phenomenon and propose a general
and novel research problem of generalized graph anomaly detection that aims to
effectively identify anomalies on both the training-domain graph and unseen
testing graph to eliminate potential dangers. Nevertheless, it is a challenging
task since only limited labels are available, and the normal background may
differ between training and testing data. Accordingly, we propose a data
augmentation method named \textit{AugAN} (\uline{Aug}mentation for
\uline{A}nomaly and \uline{N}ormal distributions) to enrich training data and
boost the generalizability of GAD models. Experiments verify the effectiveness
of our method in improving model generalizability.