Subgraph Attention for Node Classification and Hierarchical Graph Pooling

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.