Anomaly Detection in Networks via Score-Based Generative Models
Abstract
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.
Cite
Text
Gavrilev and Burnaev. "Anomaly Detection in Networks via Score-Based Generative Models." ICML 2023 Workshops: SPIGM, 2023.Markdown
[Gavrilev and Burnaev. "Anomaly Detection in Networks via Score-Based Generative Models." ICML 2023 Workshops: SPIGM, 2023.](https://mlanthology.org/icmlw/2023/gavrilev2023icmlw-anomaly/)BibTeX
@inproceedings{gavrilev2023icmlw-anomaly,
title = {{Anomaly Detection in Networks via Score-Based Generative Models}},
author = {Gavrilev, Dmitrii and Burnaev, Evgeny},
booktitle = {ICML 2023 Workshops: SPIGM},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/gavrilev2023icmlw-anomaly/}
}