  Detecting unusual patterns in graph data is a crucial task in data mining.
However, existing methods face challenges in consistently achieving
satisfactory performance and often lack interpretability, which hinders our
understanding of anomaly detection decisions. In this paper, we propose a novel
approach to graph anomaly detection that leverages the power of
interpretability to enhance performance. Specifically, our method extracts an
attention map derived from gradients of graph neural networks, which serves as
a basis for scoring anomalies. Notably, our approach is flexible and can be
used in various anomaly detection settings. In addition, we conduct theoretical
analysis using synthetic data to validate our method and gain insights into its
decision-making process. To demonstrate the effectiveness of our method, we
extensively evaluate our approach against state-of-the-art graph anomaly
detection techniques on real-world graph classification and wireless network
datasets. The results consistently demonstrate the superior performance of our
method compared to the baselines.
