  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.

Machine Learning;Artificial Intelligence