  Unsupervised GAD methods assume the lack of anomaly labels, i.e., whether a
node is anomalous or not. One common observation we made from previous
unsupervised methods is that they not only assume the absence of such anomaly
labels, but also the absence of class labels (the class a node belongs to used
in a general node classification task). In this work, we study the utility of
class labels for unsupervised GAD; in particular, how they enhance the
detection of structural anomalies. To this end, we propose a Class Label-aware
Graph Anomaly Detection framework (CLAD) that utilizes a limited amount of
labeled nodes to enhance the performance of unsupervised GAD. Extensive
experiments on ten datasets demonstrate the superior performance of CLAD in
comparison to existing unsupervised GAD methods, even in the absence of
ground-truth class label information. The source code for CLAD is available at
\url{https://github.com/jhkim611/CLAD}.
