This paper proposes a generative framework for graph-based semi-supervised learning for approximating the joint distribution of the graph structure, labels and the node features. Variational inference techniques are then used to approximate the Bayesian posterior. The paper is well written. There are some issues raised by reviewer 3 regarding a better positioning of GenGNN with respect to GCN/GAT; which are recommended to be taken into account for the final version of the paper.