Gao Yue, Zhang Zizhao, Lin Haojie, Zhao Xibin, Du Shaoyi, Zou Changqing
IEEE Trans Pattern Anal Mach Intell. 2022 May;44(5):2548-2566. doi: 10.1109/TPAMI.2020.3039374. Epub 2022 Apr 1.
Hypergraph learning is a technique for conducting learning on a hypergraph structure. In recent years, hypergraph learning has attracted increasing attention due to its flexibility and capability in modeling complex data correlation. In this paper, we first systematically review existing literature regarding hypergraph generation, including distance-based, representation-based, attribute-based, and network-based approaches. Then, we introduce the existing learning methods on a hypergraph, including transductive hypergraph learning, inductive hypergraph learning, hypergraph structure updating, and multi-modal hypergraph learning. After that, we present a tensor-based dynamic hypergraph representation and learning framework that can effectively describe high-order correlation in a hypergraph. To study the effectiveness and efficiency of hypergraph generation and learning methods, we conduct comprehensive evaluations on several typical applications, including object and action recognition, Microblog sentiment prediction, and clustering. In addition, we contribute a hypergraph learning development toolkit called THU-HyperG.
超图学习是一种在超图结构上进行学习的技术。近年来，超图学习因其在建模复杂数据相关性方面的灵活性和能力而受到越来越多的关注。在本文中，我们首先系统地回顾了关于超图生成的现有文献，包括基于距离、基于表示、基于属性和基于网络的方法。然后，我们介绍了超图上现有的学习方法，包括直推式超图学习、归纳式超图学习、超图结构更新和多模态超图学习。在此之后，我们提出了一个基于张量的动态超图表示和学习框架，该框架可以有效地描述超图中的高阶相关性。为了研究超图生成和学习方法的有效性和效率，我们对几个典型应用进行了综合评估，包括对象和动作识别、微博情感预测和聚类。此外，我们贡献了一个名为THU-HyperG的超图学习开发工具包。
