NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID: 3507 Semi-supervisedly Co-embedding Attributed Networks

### Reviewer 3

This paper proposes a novel semi-supervised co-embedding model for attributed networks, based on Semi-supervised VAE. The model design is reasonable, by considering the dependency of node-node and node-attribute in five different cases. The inference process also makes sense. It improves the existing unsupervised co-embedding model by learning also with partially labeled nodes in a semi-supervised way. The introduced model outperforms the state-of-the-art attributed graph embedding models. The paper is also well written and easy to follow. There are some writing errors to correct and some unclear notations to clarify. Please see the “improvements” part. Another suggestion is about the evaluation of the performance on different ratio of labelled data. It will be better to include it in the main text, than leaving it in the supplementary document, because the semi-supervised learning models should be evaluated on the capability of learning from different small portions of labeled data.