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Multi-view parallel graph pooling

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Abstract

Graph pooling is a crucial operation in graph neural networks (GNNs) for down-sampling. It is noted that existing methods for graph pooling suffer from two main issues. First, pooling methods based on node dropping only evaluate the importance of nodes from a single perspective, such as attention or node distance. However, the importance scores of a node are different when evaluating it from different aspects, and thus the importance of a node cannot be evaluated comprehensively only based on one single view. Second, necessary information about graph structure will be lost when nodes with lower scores are discarded. It can even result in the coarsened graph being too sparse and further lead to poor performance in related learning tasks, such as graph classification. To address these issues, we propose an attention-based multi-view parallel graph pooling method. Specifically, to comprehensively evaluate the importance of nodes, we propose to evaluate the importance node from its features, local topology structure, and global topology structure via the attention mechanism. Moreover, to alleviate the problem of information loss caused by node discarding in graph pooling, we introduce the concept of multi-view parallel pooling, which conducts graph pooling from node features, local topology, and global topology structure three views, respectively, and then integrates the three generated coarsened graphs and obtains an informative graph. Experimental results on four benchmark datasets demonstrate the effectiveness of our proposed method for graph classification.

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Data Availability

The experimental datasets are available at https://chrsmrrs.github.io/datasets/docs/datasets/.

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Acknowledgements

This work is supported by the Natural Science Foundation of China: 61806005, the University Synergy Innovation Program of Anhui Province: GXXT-2022-052 and GXXT-2020-012, the Outstanding Young Talents Support Program of Anhui Province: gxyqZD2022032, and the Natural Science Foundation of the Educational Commission of Anhui Province of China: KJ2021A0373.

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Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University of Technology, Anhui, China

    Jun Huang & Yuan-Yuan Wang

  2. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

    Jun Huang

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  1. Jun Huang
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  2. Yuan-Yuan Wang
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Yuanyuan Wang constructed the model and experiments, Jun Huang wrote the main manuscript. All authors reviewed the manuscript.

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Correspondence to Jun Huang.

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Huang, J., Wang, YY. Multi-view parallel graph pooling. Int J Data Sci Anal (2023). https://doi.org/10.1007/s41060-023-00476-8

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