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HGNN+: General Hypergraph Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Graph 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 +3 more
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Representations of hypergraph states with neural networks*
Communications in Theoretical Physics, 2021Abstract 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 +1 more
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Totally Dynamic Hypergraph Neural Networks
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023Recent 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 +5 more
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Hypergraph Convolutional Recurrent Neural Network
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020In 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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Hypergraph analysis of neural networks
Physica D: Nonlinear Phenomena, 1989zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jeffries, Clark, van den Driessche, P.
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Hypergraphs: Organizing complex natural neural networks
2005 3rd International Conference on Intelligent Sensing and Information Processing, 2005Data from neuroscience research has shown that the brain can be studied as a neural network. In view of the brain's seemingly infinite complexity, we organize the entire network into a series of sub-networks, each of whose functionalities combine to become the knowledge representation capability of the entire network.
null Eakta Jain +6 more
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Hypergraphs and Neural Networks
1991It is certainly desirable to have mathematically rigorous knowledge of the attractors of neural network models. In fact, for those models to be used in content addressable memory (static memories) or robot control, one usually seeks some assurance that the only attractors are constant trajectories built into the model and in particular that no limit ...
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Hypergraph Neural Networks for Hypergraph Matching
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021Xiaowei Liao, Yong Xu, Haibin Ling
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Hypergraph Neural Network Hawkes Process
2022 International Joint Conference on Neural Networks (IJCNN), 2022Zi-Hao Cheng, Jian-Wei Liu, Ze Cao
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Deep Hypergraph Neural Networks with Tight Framelets
Proceedings of the AAAI Conference on Artificial IntelligenceHypergraphs 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 +6 more
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