  Graph generation is an extremely important task, as graphs are found
throughout different areas of science and engineering. In this work, we focus
on the modern equivalent of the Erdos-Renyi random graph model: the graph
variational autoencoder (GVAE). This model assumes edges and nodes are
independent in order to generate entire graphs at a time using a multi-layer
perceptron decoder. As a result of these assumptions, GVAE has difficulty
matching the training distribution and relies on an expensive graph matching
procedure. We improve this class of models by building a message passing neural
network into GVAE's encoder and decoder. We demonstrate our model on the
specific task of generating small organic molecules
