Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: This paper introduces active learning for graphs using graph neural networks

The bound is not very meaningful as it requires unrealistic assumptions and is loose. 

Figure 2 shows that even random selection performs quite well compared to this elaborate method. 

This Area if research and the data sets don’t seem to have many actual real applications in the world with much impact. 

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review_title: Official Blind Review #2

final_score: rating: 8: Accept
review_content: The authors propose an interesting method to actively select samples using the embeddings learned from GNNs. The proposed method combines graph embeddings and clustering to intelligently select new node samples. Theoretical analysis is provided to support the effectiveness and experimental results shows that this method can outperform many other active learning methods.  
This paper can be improved on the following aspects:
1.	The proposed method conducts clustering using node embeddings. Although these embeddings have encoded graph structure to some extent, I would suggest explicitly incorporating the graph structure in clustering or at least comparing to a baseline on that. The proposed method conducts embedding learning and clustering in two consecutive but separate steps. It would be interesting to see that the clustering can also leverage the graph information.
2.	It would be better to provide more details about network settings (some hyperparams have already been given in the paper), and more analysis would be helpful. For example, how the number of clusters affects the performance? 
3.	Is it possible to create a scenario where there are more labeled data from one cluster but less data from another cluster? In this case, should we still take equal amount of samples from different clusters?

review_title: Official Blind Review #3

final_score: rating: 1: Reject
review_content: The authors propose to incorporate active learning into the graph neural network training and claim some guarantee on the proposed method. 

I have some concerns about the correctness of the proof. For theorem 1, how is the Hoeffding applied so that the \sqrt{n} term appears? My worry is naively applying Hoeffding as is done in the proof only gives a bound on a fixed model, but in the theorem A_t is not fixed. You may need to apply a union bound or more sophisticated set cover theory to claim the result. Or if I missed something could the authors add more details on the step using Hoeffding bound to the proof?

Other than that I also feel the assumptions on theorem 1 are way too strong. Especially assumption 2 and 3. They are simply not true in application. The assumptions are so strong that the theorem, even if the proof can be fixed, is not interesting any more.

review_title: Official Blind Review #1

Meta-review: The authors propose a method of selecting nodes to label in a graph neural network setting to reduce the loss as efficiently as possible. Building atop Sener & Savarese 2017 the authors propose an alternative distance metric and clustering algorithm. In comparison to the just mentioned work, they show that their upper bound is smaller than the previous art's upper bound. While one cannot conclude from this that their algorithm is better, at least empirically the method appears to have a advantage over state of the art.

However, reviewers were concerned about the assumptions necessary to prove the theorem, despite the modifications made by the authors after the initial round. 

The work proposes a simple estimator and shows promising results but reviewers felt improvements like reducing the number of assumptions and potentially a lower bound may greatly strengthen the paper.
Decision: Reject