Paper:
Graph neural networks have gained significant interest from the research community for both node classification within a graph and graph classification within a set of graphs. Attention mechanism applied on the neighborhood of a node improves the performance of graph neural networks. Typically, it helps to identify a neighbor node which plays more important role to determine the label of the node under consideration. But in real world scenarios, a particular subset of nodes together, but not the individual nodes in the subset, may be important to determine the label of a node. To address this problem, we introduce the concept of subgraph attention for graphs. To show the efficiency of this, we use subgraph attention with graph convolution for node classification. We further use subgraph attention for the entire graph classification by proposing a novel hierarchical neural graph pooling architecture. Along with attention over the subgraphs, our pooling architecture also uses attention to determine the important nodes within a level graph and attention to determine the important levels in the whole hierarchy. Competitive performance over the state-of-the-arts for both node and graph classification shows the efficiency of the algorithms proposed in this paper.

Review:
The paper proposes a novel attention approach to graph neural networks which is applicable to both of node and graph classification. The proposed method gives an attention to a subgraph instead of a node by which importance can be controlled as a set of nodes. Further, the authors also introduce two types of attention in the hierarchical structure of the network called intra- and inter- level attention.
Considering subgraph attention would be novel and a reasonable idea. The sampling-based approach is a bit naive though it would be easy to implement. Reliability of some results are not clear for me because of the small training set.
The intra- and inter- attention approach would be a reasonable, but the relation with subgraph attention is not mentioned in my understanding. These two are independent approaches? Nothing is related to each other?
The MUTAG dataset has only 188 graphs, and so, in the smallest case in Table 3, the training data only contains 188*0.15 = 28.2 graphs. For me, learning an attention neural network with less than 30 sample is difficult to evaluate. Is there any rationale that the proposed method works on such a small dataset?
In sensitivity analysis in A.1, the performance on different max subgraph size (T) is shown, and the change of the performance is moderate. One of the main claims of the paper is that considering a subgraph (not a node) increases the performance. This results does not show the increase of the performance with the increase of the subgraph size T. Showing the performance with T = 1 can be informative to verify improvement brought by the subgraph attention.
Is Figure 3 training set or test set?