系统梳理超图神经网络最新进展，分类解析主流模型与应用。
Recent Advances in Hypergraph Neural Networks
- 按架构将超图神经网络分为5类：卷积、注意力、自编码、循环与生成模型。
- 总结各模型在计算机视觉、自然语言处理等领域的实际应用与数学机制。
- 指出现有挑战与未来方向，适合想入门或深耕该领域的研究者。
超图神经网络（HGNNs）因其在计算机视觉、复杂网络和自然语言处理等领域捕捉超图结构数据中复杂关系的能力而受到广泛关注。本文全面回顾了HGNN的最新进展，基于模型架构将主流方法分为五类：超图卷积网络（HGCNs）、超图注意力网络（HGATs）、超图自编码器（HGAEs）、超图循环网络（HGRNs）和深度超图生成模型（DHGGMs）。针对每一类，深入探讨其应用场景、数学原理、文献贡献及未解问题。最后，讨论共性挑战与潜在研究方向。本文旨在为未来HGNN的研究与应用提供有价值的参考。
原文摘要 · Abstract (English)
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 autoencoders (HGAEs), hypergraph recurrent networks (HGRNs), 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.
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