Reviews: Scalable Deep Generative Relational Model with High-Order Node Dependence

The paper was reviewed by three experts in the field. The reviewers and AC all agree that the paper contains novel contributions, but share the same opinion that it could be strengthened by addressing the reviewers' comments. In addition to the reviewers' comments such as the need to adding comparison with VGAE and its variates, the AC would like to provide some additional feedback to the authors: The AC views the paper as some kind of smart combination of edge partition model, gamma belief net, and Dirichlet belief net, enhanced by adding covariate dependence and by incorporate the network information in learning the connection weights of the Dirichlet belief net. Pros: 1) the combination is non-trival: replacing the gamma weights in edge partition model with latent counts is the key to allow closed-form Gibbs sampling (upward latent count propagation followed by downward variable sampling). How the X is used in (3) and sampled in (5) is novel.