Unsupervised graph anomaly detection aims at identifying rare patterns that
deviate from the majority in a graph without the aid of labels, which is
important for a variety of real-world applications. Recent advances have
utilized Graph Neural Networks (GNNs) to learn effective node representations
by aggregating information from neighborhoods. This is motivated by the
hypothesis that nodes in the graph tend to exhibit consistent behaviors with
their neighborhoods. However, such consistency can be disrupted by graph
anomalies in multiple ways. Most existing methods directly employ GNNs to learn
representations, disregarding the negative impact of graph anomalies on GNNs,
resulting in sub-optimal node representations and anomaly detection
performance. While a few recent approaches have redesigned GNNs for graph
anomaly detection under semi-supervised label guidance, how to address the
adverse effects of graph anomalies on GNNs in unsupervised scenarios and learn
effective representations for anomaly detection are still under-explored. To
bridge this gap, in this paper, we propose a simple yet effective framework for
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection (G3AD).
Specifically, G3AD introduces two auxiliary networks along with correlation
constraints to guard the GNNs from inconsistent information encoding.
Furthermore, G3AD introduces an adaptive caching module to guard the GNNs from
solely reconstructing the observed data that contains anomalies. Extensive
experiments demonstrate that our proposed G3AD can outperform seventeen
state-of-the-art methods on both synthetic and real-world datasets.

本文提出了一个简单而有效的框架 G3AD，它引入了两个辅助网络和相关性约束来保护 GNN 免受不一致信息编码的影响。此外，G3AD 还引入了一个自适应缓存模块，以防止 GNN 仅重构包含异常的观察数据。大量实验证明，我们提出的 G3AD 在合成数据集和实际数据集上均能优于 17 个最先进的方法。