  We present an approach to synthesizing new graph structures from empirically
specified distributions. The generative model is an auto-decoder that learns to
synthesize graphs from latent codes. The graph synthesis model is learned
jointly with an empirical distribution over the latent codes. Graphs are
synthesized using self-attention modules that are trained to identify likely
connectivity patterns. Graph-based normalizing flows are used to sample latent
codes from the distribution learned by the auto-decoder. The resulting model
combines accuracy and scalability. On benchmark datasets of large graphs, the
presented model outperforms the state of the art by a factor of 1.5 in mean
accuracy and average rank across at least three different graph statistics,
with a 2x speedup during inference.
