文献类型：期刊文献

英文题名：Survey of?Hypergraph Neural Networks and?Its Application to?Action Recognition

作者：Wang, Cheng[1]; Ma, Nan[2]; Wu, Zhixuan[1]; Zhang, Jin[1]; Yao, Yongqiang[1]

第一作者：Wang, Cheng

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] Beijing University of Technology, Beijing, 100124, China

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2022

卷号：13605 LNAI

起止页码：387-398

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20230713597079)

语种：英文

外文关键词：Complex networks - Data handling - Deep learning

摘要：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. ? 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.