  Generating graph structures is a challenging problem due to the diverse
representations and complex dependencies among nodes. In this paper, we
introduce Graph Variational Recurrent Neural Network (GraphVRNN), a
probabilistic autoregressive model for graph generation. Through modeling the
latent variables of graph data, GraphVRNN can capture the joint distributions
of graph structures and the underlying node attributes. We conduct experiments
on the proposed GraphVRNN in both graph structure learning and attribute
generation tasks. The evaluation results show that the variational component
allows our network to model complicated distributions, as well as generate
plausible structures and node attributes.
