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Simple hierarchical PageRank graph neural networks

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Abstract

Graph neural networks (GNNs) have many variants for graph representation learning. Several works introduce PageRank into GNNs to improve its neighborhood aggregation capabilities. However, these methods leverage the general PageRank to perform complex neighborhood aggregation to obtain the final feature representation, which leads to high computational cost and oversmoothing. In this paper, we propose simple hierarchical PageRank graph neural networks (SHP-GNNs), which first utilize the simple PageRank to aggregate different neighborhood ranges of each node and then leverage a jumping architecture to combine these aggregated features to enable hierarchical structure-aware representation. In this case, first, the simple PageRank turns the neighborhood aggregation process to no-learning, thereby reducing the computational complexity of the model. Then, the jumping structure combines the aggregation features of each node’s different hierarchy (neighborhood range) to learn more informative feature representation. Finally, the successful combination of the above methods alleviates the oversmoothing problem of deep GNNs. Our experimental evaluation demonstrates that SHP-GNNs achieve or match state-of-the-art results in node classification tasks, text classification tasks, and community prediction tasks. Moreover, since SHP-GNNs’ neighborhood aggregation is a no-learning process, SHP-GNNs are more suitable for node clustering tasks.

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Data availability

This declaration is “not applicable.” Data will be made available on request.

Notes

  1. https://github.com/youngflyasd/SHP-GNN.

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Funding

This document is the results of the research project funded by the National Natural Science Foundation of China (Grant No. 61772386).

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Authors and Affiliations

  1. China North Communication Technology Co., Ltd, Wuhan Branch, Wuhan, China

    Fei Yang

  2. School of Computer Science, Wuhan University, Wuhan, China

    Huyin Zhang & Shiming Tao

  3. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

    Xiying Fan

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  1. Fei Yang
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  2. Huyin Zhang
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  3. Shiming Tao
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  4. Xiying Fan
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Contributions

FY contributed to methodology, modeling, coding, and writing. HZ was involved in supervision. ST provided software and contributed to coding. XF was involved in data and visualization.

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Correspondence to Fei Yang.

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Yang, F., Zhang, H., Tao, S. et al. Simple hierarchical PageRank graph neural networks. J Supercomput 80, 5509–5539 (2024). https://doi.org/10.1007/s11227-023-05666-6

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