  Graph pooling is an essential ingredient of Graph Neural Networks (GNNs) in
graph classification and regression tasks. For these tasks, different pooling
strategies have been proposed to generate a graph-level representation by
downsampling and summarizing nodes' features in a graph. However, most existing
pooling methods are unable to capture distinguishable structural information
effectively. Besides, they are prone to adversarial attacks. In this work, we
propose a novel pooling method named as {HIBPool} where we leverage the
Information Bottleneck (IB) principle that optimally balances the
expressiveness and robustness of a model to learn representations of input
data. Furthermore, we introduce a novel structure-aware Discriminative Pooling
Readout ({DiP-Readout}) function to capture the informative local subgraph
structures in the graph. Finally, our experimental results show that our model
significantly outperforms other state-of-art methods on several graph
classification benchmarks and more resilient to feature-perturbation attack
than existing pooling methods.
