  Graph anomaly detection in this paper aims to distinguish abnormal nodes that
behave differently from the benign ones accounting for the majority of
graph-structured instances. Receiving increasing attention from both academia
and industry, yet existing research on this task still suffers from two
critical issues when learning informative anomalous behavior from graph data.
For one thing, anomalies are usually hard to capture because of their subtle
abnormal behavior and the shortage of background knowledge about them, which
causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority
of objects in real-world graphs are normal, bringing the class imbalance
problem as well. To bridge the gaps, this paper devises a novel Data
Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed
graphs, equipped with three specially designed modules: 1) an information
fusion module employing graph neural network encoders to learn representations,
2) a graph data augmentation module that fertilizes the training set with
generated samples, and 3) an imbalance-tailored learning module to discriminate
the distributions of the minority (anomalous) and majority (normal) classes. A
series of experiments on three datasets prove that DAGAD outperforms ten
state-of-the-art baseline detectors concerning various mostly-used metrics,
together with an extensive ablation study validating the strength of our
proposed modules.
