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Graph pooling is an essential ingredient of Graph Neural Networks (GNNs) ingraph classification and regression tasks. For these tasks, different poolingstrategies have been proposed to generate a graph-level representation bydownsampling and summarizing nodes' features in a graph. However, most existingpooling methods are unable to capture distinguishable structural informationeffectively. Besides, they are prone to adversarial attacks. In this work, wepropose a novel pooling method named as {HIBPool} where we leverage theInformation Bottleneck (IB) principle that optimally balances theexpressiveness and robustness of a model to learn representations of inputdata. Furthermore, we introduce a novel structure-aware Discriminative PoolingReadout ({DiP-Readout}) function to capture the informative local subgraphstructures in the graph. Finally, our experimental results show that our modelsignificantly outperforms other state-of-art methods on several graphclassification benchmarks and more resilient to feature-perturbation attackthan existing pooling methods.

Structure-Aware Hierarchical Graph Pooling using Information Bottleneck