  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.
