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CAAI International Conference on Artificial Intelligence

CICAI 2022: Artificial Intelligence pp 387–398Cite as

Survey of Hypergraph Neural Networks and Its Application to Action Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13605))

Abstract

With the development of deep learning, graph neural networks have attracted ever-increasing attention due to their exciting results on handling data from non-Euclidean space in recent years. However, existing graph neural networks frameworks are designed based on simple graphs, which limits their ability to handle data with complex correlations. Therefore, in some special cases, especially when the data have interdependence, the complexity of the data poses a significant challenge to traditional graph neural networks algorithm. To overcome this challenge, researchers model the complex relationship of data by constructing hypergraph, and use hypergraph neural networks to learn the complex relationship within data, so as to effectively obtain higher-order feature representations of data. In this paper, we first review the basics of hypergraph, then provide a detailed analysis and comparison of some recently proposed hypergraph neural networks algorithm, next some applications of hypergraph neural networks for action recognition are listed, and finally propose potential future research directions of hypergraph neural networks to provide ideas for subsequent research.

This work was supported by Beijing Natural Science Foundation (No. 4222025), the National Natural Science Foundation of China (Nos. 61871038 and 61931012).

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Acknowledgements

This work was supported by Beijing Natural Science Foundation (No. 4222025), the National Natural Science Foundation of China (Nos. 61871038 and 61931012).

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Authors and Affiliations

  1. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China

    Cheng Wang, Zhixuan Wu, Jin Zhang & Yongqiang Yao

  2. Beijing University of Technology, Beijing, 100124, China

    Nan Ma

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  1. Cheng Wang
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  2. Nan Ma
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  3. Zhixuan Wu
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  4. Jin Zhang
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  5. Yongqiang Yao
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Corresponding author

Correspondence to Nan Ma .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Xiaomi Inc., Beijing, China

    Daniel Povey

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. JD Explore Academy, Beijing, China

    Tao Mei

  5. Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

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Cite this paper

Wang, C., Ma, N., Wu, Z., Zhang, J., Yao, Y. (2022). Survey of Hypergraph Neural Networks and Its Application to Action Recognition. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_32

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  • DOI: https://doi.org/10.1007/978-3-031-20500-2_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20499-9

  • Online ISBN: 978-3-031-20500-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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