  Graph anomaly detection (GAD) has attracted increasing attention in machine
learning and data mining. Recent works have mainly focused on how to capture
richer information to improve the quality of node embeddings for GAD. Despite
their significant advances in detection performance, there is still a relative
dearth of research on the properties of the task. GAD aims to discern the
anomalies that deviate from most nodes. However, the model is prone to learn
the pattern of normal samples which make up the majority of samples. Meanwhile,
anomalies can be easily detected when their behaviors differ from normality.
Therefore, the performance can be further improved by enhancing the ability to
learn the normal pattern. To this end, we propose a normality learning-based
GAD framework via multi-scale contrastive learning networks (NLGAD for
abbreviation). Specifically, we first initialize the model with the contrastive
networks on different scales. To provide sufficient and reliable normal nodes
for normality learning, we design an effective hybrid strategy for normality
selection. Finally, the model is refined with the only input of reliable normal
nodes and learns a more accurate estimate of normality so that anomalous nodes
can be more easily distinguished. Eventually, extensive experiments on six
benchmark graph datasets demonstrate the effectiveness of our normality
learning-based scheme on GAD. Notably, the proposed algorithm improves the
detection performance (up to 5.89% AUC gain) compared with the state-of-the-art
methods. The source code is released at https://github.com/FelixDJC/NLGAD.
