Discrete Latent Graph Generative Modeling with Diffusion Bridges
Van Khoa Nguyen Yoann Boget Frantzeska Lavda Alexandros Kalousis
Learning graph generative models over latent spaces has received less
attention compared to models that operate on the original data space and has so
far demonstrated lacklustre performance. We present GLAD a latent space graph
generative model. Unlike most previous latent space graph generative models,
GLAD operates on a discrete latent space that preserves to a significant extent
the discrete nature of the graph structures making no unnatural assumptions
such as latent space continuity. We learn the prior of our discrete latent
space by adapting diffusion bridges to its structure. By operating over an
appropriately constructed latent space we avoid relying on decompositions that
are often used in models that operate in the original data space. We present
experiments on a series of graph benchmark datasets which clearly show the
superiority of the discrete latent space and obtain state of the art graph
generative performance, making GLAD the first latent space graph generative
model with competitive performance. Our source code is published at:
\url{https://github.com/v18nguye/GLAD}.