Graph neural networks get significant attention for graph representation and classification in machine learning community. Different types of neighborhood aggregation and pooling strategies have been proposed in the literature. In this work, we introduce a higher order hierarchical GNN algorithm (SubGattPool) by employing (i) an attention mechanism which learns the importance and aggregates neighboring subgraphs of a node instead of first-order neighbors, and (ii) a hierarchical pooling strategy which learns the importance of different hierarchies in a GNN. SubGattPool is able to achieve state-of-the-art graph classification performance on multiple real-world datasets.

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