Hierarchical Graph Representation Learning with Differentiable Pooling

NeurIPS 2018 Rex YingJiaxuan YouChristopher MorrisXiang RenWilliam L. HamiltonJure Leskovec

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchical representations of graphs---a limitation that is especially problematic for the task of graph classification, where the goal is to predict the label associated with an entire graph... (read more)

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