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.