Hypergraph Learning: Methods and Practices

By Yue Gao, Zizhao Zhang, Haojie Lin, Xibin Zhao, S. Du, Changqing Zou. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020

378 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4d3283c50bcf41b9ad7a29a1914714b911b7f0ff

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Citations in the corpus

UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks (2021, background): “Recently, hypergraphs have drawn the attention from a wide range of fields, like computer vision [Gao et al., 2020; Liu et al., 2020], recommendation system [Xia et al.”
Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, methodology): “Gao et al. [10] provided insights into the methods and enumerated some applications of hypergraphs in their study.”
Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation (2024, neutral): “In recent years, research on RCNN-based methods has diminished, particularly in scenarios demanding real-time detection, limiting their development and applicability.”
Hypergraph Contrastive Collaborative Filtering (2022, methodology): “Inspired by the generalization ability of hypergraph in modeling complex high-order dependencies [8, 9, 11], some recently developed recommender systems are empowered to capture interaction patterns with the constructed hypergraph structures and uniform node-hyperedge connections, like HyRec [33], D”
YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception (2025, neutral): “Complex multi-to-multi high-order correlations are ubiquitous in nature, e.g ., neural connections and protein interactions, as well as in the field of information science, e.g ., social networks [31], [32].”