  Graph-based anomaly detection has been widely used for detecting malicious
activities in real-world applications. Existing attempts to address this
problem have thus far focused on structural feature engineering or learning in
the binary classification regime. In this work, we propose to leverage graph
contrastive coding and present the supervised GraphCAD model for contrasting
abnormal nodes with normal ones in terms of their distances to the global
context (e.g., the average of all nodes). To handle scenarios with scarce
labels, we further enable GraphCAD as a self-supervised framework by designing
a graph corrupting strategy for generating synthetic node labels. To achieve
the contrastive objective, we design a graph neural network encoder that can
infer and further remove suspicious links during message passing, as well as
learn the global context of the input graph. We conduct extensive experiments
on four public datasets, demonstrating that 1) GraphCAD significantly and
consistently outperforms various advanced baselines and 2) its self-supervised
version without fine-tuning can achieve comparable performance with its fully
supervised version.
