Venue: ICLR 2018 Workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper provides an empirical comparison of some unsupervised graph node representation algorithms, by measuring classification performance in logistic regression.

It's interesting that ignoring the directed edges in the graph gives better performance, even in the methods designed for use with directed graphs. I don't think that observation alone on a couple of datasets, without further insight is quite enough.

The abstract promises that the results give insights into the properties of node embedding algorithms. However, there really isn't any analysis of what underlies the results, or why they apparently contradict previous work. As the work isn't "extremely novel", I think it needs to be a little further a long.
review_title: Empirical comparison of node embedding

final_score: rating: null
review_content: The task described in the intro is interesting. But the node classification task is a bit unclear. There are multiple centrality measures but only one classification task. It is also unclear which metric was used in the classification task. And, why is it not a regression task? Say, predict the actual betweeness centrality metric rather than a class.

Moreover, there are a number of other node embedding methods HolE, RESCAL, Molecular fingerprints (Duvenaud et al.), GraphSAGE, GraphNN, etc.. It is unclear why we should settle for only LINE and node2vec, and what it tells us about the other methods.

Please clarify.

 

review_title: Task needs clarification

final_score: rating: 4: Ok but not good enough - rejection
review_content: The idea overall is interesting: whether graph embeddings for a simple node classification task correlate with graph-wide metrics (such as centrality).  For an empirical workshop paper seeking to compare embedding methods, you would expect to find a lot more methods in the list. After asking the authors some questions, I am not convinced why other methods were not used.  The results are a bit confusing and the conclusion vague.

* GraphSAGE is basically a variant of Duvenaud that deals with the varying neighborhood sizes. The principle is the same.

PS: Please avoid keeping vital information about the experiments limited to the figure captions.

PS2: I am not as confident the results of the methods presented in the paper generalize to other methods. There is very little reason to believe that it would.
review_title: Interesting but not quite there yet

Meta-review: Based on the reviews, this paper has not been accepted for presentation at the ICLR workshop. However, the conversation and updates can continue to appear here on OpenReview.
Decision: Reject