Anomaly Detection in Networks via Score-Based Generative Models
This is an incremental approach to a domain-specific problem in graph anomaly detection.
The paper tackled node outlier detection in attributed graphs by incorporating score-based generative models, achieving competitive results on small-scale graphs and analyzing challenges in reconstructing Dirichlet energy.
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.