Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message
  Passing and Hyperbolic Neural Networks
Jing Gu Dongmian Zou
Graph anomaly detection plays a vital role for identifying abnormal instances
in complex networks. Despite advancements of methodology based on deep learning
in recent years, existing benchmarking approaches exhibit limitations that
hinder a comprehensive comparison. In this paper, we revisit datasets and
approaches for unsupervised node-level graph anomaly detection tasks from three
aspects. Firstly, we introduce outlier injection methods that create more
diverse and graph-based anomalies in graph datasets. Secondly, we compare
methods employing message passing against those without, uncovering the
unexpected decline in performance associated with message passing. Thirdly, we
explore the use of hyperbolic neural networks, specifying crucial architecture
and loss design that contribute to enhanced performance. Through rigorous
experiments and evaluations, our study sheds light on general strategies for
improving node-level graph anomaly detection methods.