Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models
Published in
arXiv.org(2022)External Links:
TL
TL;DR
GODM, a novel data augmentation for mitigating class imbalance in supervised Graph Outlier detection with latent Diffusion Models is introduced and the case study further demonstrated the generation quality of the synthetic data.