Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled. When the labelling status depends on the unobserved node response, ignoring the missingness can lead to significant estimation bias and handicap the classifiers. This situation is called non-ignorable non-response. To solve the problem, we propose a Graph-based joint model with Non-ignorable Non-response (GNN), followed by a joint inverse weighting estimation procedure incorporated with sampling imputation approach. Our method is proved to outperform some state-of-art models in both regression and classification problems, by simulations and real analysis on the Cora dataset.

I think this claim should be motivated well enough, at least to me it was not entirely clear why this is important. If authors can provide some scenarios which can help understand the claim, it will be beneficial for the readers. However, the experimental analysis can be improved. One of the baselines the authors consider is "SM", but do not mention the paper in which it is proposed. They should produce results on multiple real world datasets. However, authors do not compare the proposed model with state of the art graph based SSL methods like GAT (Velickovic et al., ICML 2018) etc. [Velickovic et al., ICML 2018] Graph Attention Networks 4. Minor points: -- "vertexes" - "vertices" -- Not sure if using gradient descent qualifies as a contribution.

This paper considers graph-based semi-supervised learning when missing labels are not missing at random (NMAR), in other words, the absence of a label is'nonignorable'. It introduces a graphical neural network that models the relationship between the presence or absence of a label and the labels of its neighbors. The paper also proves that this model is identifiable. The reviewers agree that studying NMAR labels in the context of neural graph embeddings is a novel and important topic. They make several suggestions for improvement, including comparing with recent work such as Velickovic et al., ICML 2018.

Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled. When the labelling status depends on the unobserved node response, ignoring the missingness can lead to significant estimation bias and handicap the classifiers. This situation is called non-ignorable non-response. To solve the problem, we propose a Graph-based joint model with Non-ignorable Non-response (GNN), followed by a joint inverse weighting estimation procedure incorporated with sampling imputation approach. Our method is proved to outperform some state-of-art models in both regression and classification problems, by simulations and real analysis on the Cora dataset.

Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled. When the labelling status depends on the unobserved node response, ignoring the missingness can lead to significant estimation bias and handicap the classifiers. This situation is called non-ignorable non-response. To solve the problem, we propose a Graph-based joint model with Non-ignorable Non-response (GNN), followed by a joint inverse weighting estimation procedure incorporated with sampling imputation approach. Our method is proved to outperform some state-of-art models in both regression and classification problems, by simulations and real analysis on the Cora dataset.