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Dynamic Hypergraph Neural Networks [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 In recent years, graph/hypergraph-based deep learning methods have attracted much attention from researchers. These deep learning methods take graph/hypergraph structure as prior knowledge in the model. However, hidden and important relations are not directly represented in the inherent structure.
Jianwen Jiang +4 more
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Sheaf Hypergraph Networks [PDF]
, 2023 Higher-order relations are widespread in nature, with numerous phenomena involving complex interactions that extend beyond simple pairwise connections.
Giulia Cassarà +3 more
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Residual Enhanced Multi-Hypergraph Neural Network [PDF]
2021 IEEE International Conference on Image Processing (ICIP), 2021 Hypergraphs are a generalized data structure of graphs to model higher-order correlations among entities, which have been successfully adopted into various research domains. Meanwhile, HyperGraph Neural Network (HGNN) is currently the de-facto method for hypergraph representation learning. However, HGNN aims at single hypergraph learning and uses a pre-
Jing Huang, Xiaolin Huang, Jie Yang 0002
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Stability and Generalization of Hypergraph Collaborative Networks
, 2023 Graph neural networks have been shown to be very effective in utilizing pairwise relationships across samples. Recently, there have been several successful proposals to generalize graph neural networks to hypergraph neural networks to exploit more ...
Yip, Andy, Wu, Hanrui, Ng, Michael
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Let There be Direction in Hypergraph Neural Networks. [PDF]
Trans. Mach. Learn. Res.Hypergraphs are a powerful abstraction for modeling high-order interactions between a set of entities of interest and have been attracting a growing interest in the graph-learning literature. In particular, directed hypegraphs are crucial in their capability of representing real-world phenomena involving group relations where two sets of elements ...
Fiorini S. +3 more
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Message Passing Neural Networks for Hypergraphs
, 2022 Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects. In this work, we present a new graph neural network based on message passing capable of processing hypergraph-structured data.
Sajjad Heydari, Lorenzo Livi
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Cross-modal Hypergraph Optimisation Learning for Multimodal Sentiment Analysis [PDF]
Jisuanji kexueSentiment expressions are multimodal,and more accurate emotions can be derived through multiple modalities such as verbal,audio,and visual.Studying the interactions among modalities can effectively improve the accuracy of multimodal sentiment analysis ...
JIANG Kun, ZHAO Zhengpeng, PU Yuanyuan, HUANG Jian, GU Jinjing, XU Dan
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T-HyperGNNs: Hypergraph Neural Networks Via Tensor Representations
, 2023 Hypergraph neural networks (HyperGNNs) are a family of deep neural networks designed to perform inference on hypergraphs. HyperGNNs follow either a spectral or a spatial approach, in which a convolution or message-passing operation is conducted based on ...
Wei Qian (14588036) +3 more
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, 2023 This open access book discusses the theory and methods of hypergraph computation. Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc ...
Gao, Yue, Dai, Qionghai
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Hypergraph Neural Networks for Cross-domain Text-to-SQL [PDF]
Jisuanji gongchengGraph Neural Network (GNN) have been widely used as encoders in recent years for cross-domain Text-to-SQL. The encoding process based on GNN substantially improves the generalization of generative models under cross-domain Text-to-SQL by capturing the ...
HAO Zhifeng, LI Yanglin, XU Boyan, CAI Ruichu
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