Reviews: Conditional Structure Generation through Graph Variational Generative Adversarial Nets

Originality: The task of the conditional generation of graphs is new, as well as the constraint of permutation invariance, and the flexibility in terms of the generated graph structures (non-fixed set of nodes). The work is a combination of known techniques: a VAE-GAN architecture adapted to graphs, using graph convolutional neural networks and incorporating the permutation invariance constraint. To the best of my knowledge, the literature review is clear and related work adequately cited. Quality: This paper is technically sound and the VAE-GAN-GCN methodology is rigorously described. The authors also provide the code associated with this paper. It is definitely a complete piece of work.