Survey on Hypergraph Neural Networks

Lin Jingjing; Ye Zhonglin; Zhao Haixing; Li Zhuoran

doi:10.7544/issn1000-1239.202220483

Journal of Computer Research and Development > 2024 > 61(2): 362-384. > DOI: 10.7544/issn1000-1239.202220483 CSTR: 32373.14.issn1000-1239.202220483

Lin Jingjing, Ye Zhonglin, Zhao Haixing, Li Zhuoran. Survey on Hypergraph Neural Networks[J]. Journal of Computer Research and Development, 2024, 61(2): 362-384. DOI: 10.7544/issn1000-1239.202220483

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Survey on Hypergraph Neural Networks

Lin Jingjing^{1, 2, 3,},
Ye Zhonglin^{1, 2},
Zhao Haixing^{1, 2, ,},
Li Zhuoran^{1, 2}

1.
College of Computer, Qinghai Normal University, Xining 810008
2.
State Key Laboratory of Tibetan Intelligent Information Processing and Application (Qinghai Normal University), Xining 810008
3.
Department of Information Engineering, Xining Urban Vocational & Technical College, Xining 810003

Funds: This work was supported by the National Key Research and Development Program of China (2020YFC1523300), the Youth Program of Natural Science Foundation of Qinghai Province (2021-ZJ-946Q), and the Middle-Youth Program of Natural Science Foundation of Qinghai Normal University (2020QZR007).

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Author Bio:
Lin Jingjing: bron in 1986. PhD candidate, lecturer. Her main research interests include graph neural networks and hypergraph neural networks

Ye Zhonglin: born in 1989. PhD, associate professorr, PhD supervisor. Member of CCF. His main research interests include graph neural networks, knowledge extraction, and network representation learning

Zhao Haixing: born in 1969. PhD, professor, PhD supervisor. Member of CCF. His main research interests include complex network, graph neural networks, machine translation, hypergraph theory, and network reliability

Li Zhuoran: born in 1996. Master. His main research interests include data mining and graph neural networks
Received Date: June 09, 2022
Revised Date: April 17, 2023
Available Online: November 13, 2023

Graphical Abstract

Abstract

Abstract

In recent years, graph neural networks have achieved remarkable results in application fields such as recommendation systems and natural language processing with the help of large amounts of data and supercomputing power, and they mainly deal with graph data with pairwise relationships. However, in many real-world networks, the relationships between objects are more complex and beyond pairwise, such as scientific collaboration networks, protein networks, and others. If we directly use a graph to represent the complex relationships as pairwise relations, which will lead to a loss of information. Hypergraph is a flexible modeling tool, which shows higher-order relationships that cannot be fully described by a graph, making up for the shortage of graph. In light of this, scholars begin to care about how to develop neural networks on hypergraph, and successively put forward many hypergraph neural network models. Therefore, we overview the existing hypergraph neural network models. Firstly, we comprehensively review the development of the hypergraph neural network in the past three years. Secondly, we propose a new classification method according to the design method of hypergraph neural networks, and elaborate on representative models. Then, we introduce the application areas of hypergraph neural networks. Finally, the future research direction of hypergraph neural networks are summarized and discussed.
- hypergraph,
- hypergraph neural network,
- classification,
- graph,
- graph neural networks

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References (151)

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