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.

翻译：检测图数据中的异常模式是数据挖掘中的一项关键任务。然而，现有方法在一致达到令人满意的性能方面常常面临挑战，且缺乏可解释性，这阻碍了我们对异常检测决策的理解。本文提出了一种新颖的图异常检测方法，利用可解释性的优势来增强性能。具体而言，我们的方法提取了一种基于图神经网络梯度导出的注意力图，以此作为异常评分的依据。此外，我们利用合成数据进行了理论分析，以验证我们的方法并深入理解其决策过程。为了证明我们方法的有效性，我们与最先进的图异常检测技术进行了广泛评估。结果表明，与基线方法相比，我们的方法始终展现出优越的性能。