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Hypergraph Convolutional Recurrent Neural Network

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
In this study, we present a hypergraph convolutional recurrent neural network (HGC-RNN), which is a prediction model for structured time-series sensor network data. Representing sensor networks in a graph structure is useful for expressing structural relationships among sensors.
Jaehyuk Yi, Jinkyoo Park
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Kernelized Hypergraph Neural Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence
Hypergraph Neural Networks (HGNNs) have attracted much attention for high-order structural data learning. Existing methods mainly focus on simple mean-based aggregation or manually combining multiple aggregations to capture multiple information on hypergraphs.
Yifan Feng, Shihui Ying, Shaoyi Du
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Deep Hypergraph Neural Networks with Tight Framelets

Proceedings of the AAAI Conference on Artificial Intelligence
Hypergraphs provide a flexible framework for modeling high-order (complex) interactions among multiple entities, extending beyond traditional pairwise correlations in graph structures. However, deep hypergraph neural networks (HGNNs) often face the challenge of oversmoothing with increasing depth, similar to issues in graph neural networks (GNNs ...
Ming Li 0065, Yujie Fang, Yi Wang 0022, Han Feng, Yongchun Gu, Lu Bai 0001, Pietro Liò   +6 more
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Hypergraph Structure Learning for Hypergraph Neural Networks

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022
Hypergraphs are natural and expressive modeling tools to encode high-order relationships among entities. Several variations of Hypergraph Neural Networks (HGNNs) are proposed to learn the node representations and complex relationships in the hypergraphs. Most current approaches assume that the input hypergraph structure accurately depicts the relations
Derun Cai, Moxian Song, Chenxi Sun, Baofeng Zhang, Shenda Hong, Hongyan Li 0002   +5 more
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Hypergraph Transformer Neural Networks

ACM Transactions on Knowledge Discovery from Data, 2023
Graph neural networks (GNNs) have been widely used for graph structure learning and achieved excellent performance in tasks such as node classification and link prediction. Real-world graph networks imply complex and various semantic information and are often referred to as heterogeneous information networks (HINs).
Mengran Li 0001, Yong Zhang 0029, Xiaoyong Li, Yuchen Zhang, Baocai Yin   +4 more
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Totally Dynamic Hypergraph Neural Networks

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
Recent dynamic hypergraph neural networks (DHGNNs) are designed to adaptively optimize the hypergraph structure to avoid the dependence on the initial hypergraph structure, thus capturing more hidden information for representation learning. However, most existing DHGNNs cannot adjust the hyperedge number and thus fail to fully explore the underlying ...
Peng Zhou 0006, Zongqian Wu, Xiangxiang Zeng, Guoqiu Wen, Junbo Ma, Xiaofeng Zhu 0001   +5 more
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Representations of hypergraph states with neural networks*

Communications in Theoretical Physics, 2021
Abstract The quantum many-body problem (QMBP) has become a hot topic in high-energy physics and condensed-matter physics. With an exponential increase in the dimensions of Hilbert space, it becomes very challenging to solve the QMBP, even with the most powerful computers.
Ying 莹 Yang 杨, Huaixin 怀信 Cao 曹   +1 more
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Hypergraph neural diffusion networks

Neural Networks
We present the Hypergraph Neural Diffusion Networks (HNDiffN) for learning node embedding and hyperedge embedding in hypergraphs. The main novelty lies in developing a continuous-time diffusion equation defined on nodes and hyperedges in hypergraphs suitably.
Fengcheng Lu, Michael Ng, Andy Yip
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HGNN⁺: General Hypergraph Neural Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Graph Neural Networks have attracted increasing attention in recent years. However, existing GNN frameworks are deployed based upon simple graphs, which limits their applications in dealing with complex data correlation of multi-modal/multi-type data in practice. A few hypergraph-based methods have recently been proposed to address the problem of multi-
Yue Gao 0002, Yifan Feng 0001, Shuyi Ji, Rongrong Ji   +3 more
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Mode Hypergraph Neural Network

IEEE Transactions on Neural Networks and Learning Systems
The hypergraph neural network (HGNN) is an emerging powerful tool for modeling and learning complex, high-order correlations among entities upon hypergraph structures. While existing HGNN-based approaches excel in modeling high-order correlations among data using hyperedges, they often have difficulties in distinguishing diverse semantics (e.g ...
Shuyi Ji, Yifan Feng 0001, Donglin Di, Shihui Ying, Yue Gao 0002   +4 more
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