Reviews: Watch Your Step: Learning Node Embeddings via Graph Attention

The paper proposes a new algorithm for learning node embedding, by bringing together the attention model and the graph likelihood objective function suggested in a recent work [2]. By learning the context distribution determining the coefficients of powers of the transition matrix, it leads to a more flexible representation learning model. Quality: Although the technical content of the paper seems to be correct, I would like to point out some issues in the paper: - In Line 121, the sentence "In practice, random walk methods based on Deepwalk do not use C as a hard limit" is not generally correct because many methods (such as Node2vec) perform fixed length walks. Therefore, I think it could be also better to see its performance against the methods such as HOPE. I would strongly recommend to examine the performance of the method over different training and test sizes.