Structure-Aware Hierarchical Graph Pooling using Information Bottleneck

• URL: http://arxiv.org/abs/2104.13012v1
• Date: Tue, 27 Apr 2021 07:27:43 GMT
• Title: Structure-Aware Hierarchical Graph Pooling using Information Bottleneck
• Authors: Kashob Kumar Roy, Amit Roy, A K M Mahbubur Rahman, M Ashraful Amin and Amin Ahsan Ali
• Abstract summary: Graph pooling is an essential ingredient of Graph Neural Networks (GNNs) in graph classification and regression tasks. We propose a novel pooling method named as HIBPool where we leverage the Information Bottleneck (IB) principle. We also introduce a novel structure-aware Discriminative Pooling Readout (DiP-Readout) function to capture the informative local subgraph structures in the graph.
• Score: 2.7088996845250897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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