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<p>The growing interest in hypergraph neural networks (HGNNs) is driven by their capacity to capture the complex relationships and patterns within hypergraph structured data across various domains, including computer vision, complex networks, and natural language processing. This paper comprehensively reviews recent advances in HGNNs and presents a taxonomy of mainstream models based on their architectures: hypergraph convolutional networks (HGCNs), hypergraph attention networks (HGATs), hypergraph recurrent networks (HGRNs), hypergraph autoencoders (HGAEs), and deep hypergraph generative models (DHGGMs). For each category, we delve into its practical applications, mathematical mechanisms, literature contributions, and open problems. Finally, we discuss some common challenges and promising research directions. This paper aspires to be a helpful resource that provides guidance for future research and applications of HGNNs.</p>

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Recent Advances in Hypergraph Neural Networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月13日

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  • Mu-Rong Yang,
  • Xin-Jian Xu

摘要

The growing interest in hypergraph neural networks (HGNNs) is driven by their capacity to capture the complex relationships and patterns within hypergraph structured data across various domains, including computer vision, complex networks, and natural language processing. This paper comprehensively reviews recent advances in HGNNs and presents a taxonomy of mainstream models based on their architectures: hypergraph convolutional networks (HGCNs), hypergraph attention networks (HGATs), hypergraph recurrent networks (HGRNs), hypergraph autoencoders (HGAEs), and deep hypergraph generative models (DHGGMs). For each category, we delve into its practical applications, mathematical mechanisms, literature contributions, and open problems. Finally, we discuss some common challenges and promising research directions. This paper aspires to be a helpful resource that provides guidance for future research and applications of HGNNs.