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 unsupervised setting in most GAD studies with a fully
unlabeled graph. As expected, we find that having access to these 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 (GGAD) for the semi-supervised scenario to better
exploit the normal nodes. The key idea is to generate outlier nodes that
assimilate anomaly nodes in both local structure and node representations for
providing effective negative node samples in training a discriminative
one-class classifier. There have been many generative anomaly detection
approaches, but they are designed for non-graph data, and as a result, they
fail to take account of the graph structure information. Our approach tackles
this problem by generating graph structure-aware outlier nodes that have
asymmetric affinity separability from normal nodes while being enforced to
achieve egocentric closeness to normal nodes in the node representation space.
Comprehensive experiments on four real-world 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
this https URL

本研究提出了一种新颖的半监督场景下的图异常检测方法（GGAD），通过生成能够在本地结构和节点表示中模拟异常节点的异常节点，为训练具有区分性的单类分类器提供有效的负节点样本，从而更好地利用已知的正常节点。在四个真实世界数据集上进行了全面的实验，建立了半监督图异常检测方法的基准，并展示了 GGAD 在不同数量训练正常节点下大大优于最先进的无监督和半监督图异常检测方法。