Paper:
Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data. However, a large quantity of labeled graphs is difficult to obtain, which significantly limits the true success of GNNs. Although active learning has been widely studied for addressing label-sparse issues with other data types like text, images, etc., how to make it effective over graphs is an open question for research. In this paper, we present an investigation on active learning with GNNs for node classification tasks. Specifically, we propose a new method, which uses node feature propagation followed by K-Medoids clustering of the nodes for instance selection in active learning. With a theoretical bound analysis we justify the design choice of our approach. In our experiments on four benchmark datasets, the proposed method outperforms other representative baseline methods consistently and significantly.

Review:
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?