Node outlier detection in attributed graphs is a challenging problem for which there is no method that would work well across different datasets. Motivated by the state-of-the-art results of score-based models in graph generative modeling, we propose to incorporate them into the aforementioned problem. Our method achieves competitive results on small-scale graphs. We provide an empirical analysis of the Dirichlet energy, and show that generative models might struggle to accurately reconstruct it.

翻译：属性图中的节点异常检测是一项具有挑战性的问题，目前尚无能够在不同数据集上均表现良好的方法。受基于评分的模型在图生成建模中取得的最新成果启发，我们将其引入上述问题。所提方法在小规模图上取得了具有竞争力的结果。我们对方程能量进行了实证分析，并表明生成模型可能难以精确重构该能量。