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。

保护图神经网络的无监督图异常检测

Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection

Unsupervised graph anomaly detection is crucial for various practical
applications as it aims to identify anomalies in a graph that exhibit rare
patterns deviating significantly from the majority of nodes. Recent
advancements have utilized Graph Neural Networks (GNNs) to learn high-quality
node representations for anomaly detection by aggregating information from
neighborhoods. However, the presence of anomalies may render the observed
neighborhood unreliable and result in misleading information aggregation for
node representation learning. Selecting the proper neighborhood is critical for
graph anomaly detection but also challenging due to the absence of
anomaly-oriented guidance and the interdependence with representation learning.
To address these issues, we utilize the advantages of reinforcement learning in
adaptively learning in complex environments and propose a novel method that
incorporates Reinforcement neighborhood selection for unsupervised graph
ANomaly Detection (RAND). RAND begins by enriching the candidate neighbor pool
of the given central node with multiple types of indirect neighbors. Next, RAND
designs a tailored reinforcement anomaly evaluation module to assess the
reliability and reward of considering the given neighbor. Finally, RAND selects
the most reliable subset of neighbors based on these rewards and introduces an
anomaly-aware aggregator to amplify messages from reliable neighbors while
diminishing messages from unreliable ones. Extensive experiments on both three
synthetic and two real-world datasets demonstrate that RAND outperforms the
state-of-the-art methods.

提出了一种基于增强学习的无监督图异常检测方法（RAND），该方法利用图神经网络学习节点表示并选择可靠的邻居节点，通过增加增强学习模块和异常感知汇聚器来提高检测性能。实验证明，RAND 方法在合成和真实数据集上优于现有方法。