Interpretable Graph Anomaly Detection using Gradient Attention Maps
Yifei Yang Peng Wang Xiaofan He Dongmian Zou
Detecting unusual patterns in graph data is a crucial task in data mining.
However, existing methods often face challenges in consistently achieving
satisfactory performance and 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. 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. The results consistently demonstrate the superior
performance of our method compared to the baselines.