  Deep learning on graphs has become a popular research topic with many
applications. However, past work has concentrated on learning graph embedding
tasks, which is in contrast with advances in generative models for images and
text. Is it possible to transfer this progress to the domain of graphs? We
propose to sidestep hurdles associated with linearization of such discrete
structures by having a decoder output a probabilistic fully-connected graph of
a predefined maximum size directly at once. Our method is formulated as a
variational autoencoder. We evaluate on the challenging task of molecule
generation.
