  Exploring the graph latent structures has not garnered much attention in the
graph generative research field. Yet, exploiting the latent space is as crucial
as working on the data space for discrete data such as graphs. However,
previous methods either failed to preserve the permutation symmetry of graphs
or lacked an effective approaches to model appropriately within the latent
space. To mitigate those issues, we propose a simple, yet effective discrete
latent graph diffusion generative model. Our model, namely GLAD, not only
overcomes the drawbacks of existing latent approaches, but also alleviates
inherent issues present in diffusion methods applied on the graph space. We
validate our generative model on the molecular benchmark datasets, on which it
demonstrates competitive performance compared with the state-of-the-art
baselines.
