  This work considers a practical semi-supervised graph anomaly detection (GAD)
scenario, where part of the nodes in a graph are known to be normal,
contrasting to the extensively explored unsupervised setting with a fully
unlabeled graph. We reveal that having access to the normal nodes, even just a
small percentage of normal nodes, helps enhance the detection performance of
existing unsupervised GAD methods when they are adapted to the semi-supervised
setting. However, their utilization of these normal nodes is limited. In this
paper, we propose a novel Generative GAD approach (namely GGAD) for the
semi-supervised scenario to better exploit the normal nodes. The key idea is to
generate pseudo anomaly nodes, referred to as 'outlier nodes', for providing
effective negative node samples in training a discriminative one-class
classifier. The main challenge here lies in the lack of ground truth
information about real anomaly nodes. To address this challenge, GGAD is
designed to leverage two important priors about the anomaly nodes -- asymmetric
local affinity and egocentric closeness -- to generate reliable outlier nodes
that assimilate anomaly nodes in both graph structure and feature
representations. Comprehensive experiments on six real-world GAD datasets are
performed to establish a benchmark for semi-supervised GAD and show that GGAD
substantially outperforms state-of-the-art unsupervised and semi-supervised GAD
methods with varying numbers of training normal nodes. Code will be made
available at https://github.com/mala-lab/GGAD.
