  We develop a graph generative adversarial network to generate sparse data
sets like those produced at the CERN Large Hadron Collider (LHC). We
demonstrate this approach by training on and generating sparse representations
of MNIST handwritten digit images and jets of particles in proton-proton
collisions like those at the LHC. We find the model successfully generates
sparse MNIST digits and particle jet data. We quantify agreement between real
and generated data with a graph-based Fr\'echet Inception distance, and the
particle and jet feature-level 1-Wasserstein distance for the MNIST and jet
datasets respectively.
