  Cross-domain graph anomaly detection (CD-GAD) describes the problem of
detecting anomalous nodes in an unlabelled target graph using auxiliary,
related source graphs with labelled anomalous and normal nodes. Although it
presents a promising approach to address the notoriously high false positive
issue in anomaly detection, little work has been done in this line of research.
There are numerous domain adaptation methods in the literature, but it is
difficult to adapt them for GAD due to the unknown distributions of the
anomalies and the complex node relations embedded in graph data. To this end,
we introduce a novel domain adaptation approach, namely Anomaly-aware
Contrastive alignmenT (ACT), for GAD. ACT is designed to jointly optimise: (i)
unsupervised contrastive learning of normal representations of nodes in the
target graph, and (ii) anomaly-aware one-class alignment that aligns these
contrastive node representations and the representations of labelled normal
nodes in the source graph, while enforcing significant deviation of the
representations of the normal nodes from the labelled anomalous nodes in the
source graph. In doing so, ACT effectively transfers anomaly-informed knowledge
from the source graph to learn the complex node relations of the normal class
for GAD on the target graph without any specification of the anomaly
distributions. Extensive experiments on eight CD-GAD settings demonstrate that
our approach ACT achieves substantially improved detection performance over 10
state-of-the-art GAD methods. Code is available at
https://github.com/QZ-WANG/ACT.
