  Generative models of graph structure have applications in biology and social
sciences. The state of the art is GraphRNN, which decomposes the graph
generation process into a series of sequential steps. While effective for
modest sizes, it loses its permutation invariance for larger graphs. Instead,
we present a permutation invariant latent-variable generative model relying on
graph embeddings to encode structure. Using tools from the random graph
literature, our model is highly scalable to large graphs with likelihood
evaluation and generation in $O(|V | + |E|)$.
