Review for NeurIPS paper: Graph Stochastic Neural Networks for Semi-supervised Learning

Weaknesses: This paper combines latent variable models with GNNs, it's not novel enough and there are many previous works with similar ideas in graph generation. The difference is that the formulation of this paper is more like a conditional generative model and targets at node classification tasks. Based on the implementation of the method, I think the model is similar to RGCN in some aspects. Undoubtedly, there are differences that the model does not directly learn a Gaussian representation but instead samples from a Gaussian latent variable and concatenates it with the features of the node. However, both aim to inject some noise and in essence decrease the information between the representation and the original node feature so that the model only captures the key attributes and thus making the model more robust than vanilla GNNs.