1 code implementation • 30 Oct 2022 • Kiarash Zahirnia, Oliver Schulte, Parmis Naddaf, Ke Li
We utilize the micro-macro objective to improve graph generation with a GraphVAE, a well-established model based on graph-level latent variables, that provides fast training and generation time for medium-sized graphs.
no code implementations • 29 Jun 2021 • Kiarash Zahirnia, Ankita Sakhuja, Oliver Schulte, Parmis Nadaf, Ke Li, Xia Hu
Our experiments demonstrate a significant improvement in the realism of the generated graph structures, typically by 1-2 orders of magnitude of graph structure metrics, compared to leading graph VAEand GAN models.