Results 141 to 150 of about 1,216 (173)
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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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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 0002 +3 more
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Hypergraph neural diffusion networks
Neural NetworksWe 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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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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Hypergraph Neural Networks for Hypergraph Matching
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021Xiaowei Liao, Yong Xu 0007, Haibin Ling
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Hypergraph Neural Network for Emotion Recognition in Conversations
ACM Transactions on Asian and Low-Resource Language Information ProcessingModeling conversational context is an essential step for emotion recognition in conversations. Existing works still suffer from insufficient utilization of local context information and remote context information. This article designs a hypergraph neural network, namely HNN-ERC, to better utilize local and remote contextual information.
Xiao Sun, Haojie Xu, Cheng Zheng
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HGNNv2: Stable Hypergraph Neural Networks
IEEE Transactions on Pattern Analysis and Machine IntelligenceHypergraph neural networks (HGNNs) are widely used models for analyzing higher-order relational data. HGNNs suffer from the rapid performance degradation with increasing layers. Hypergraph dynamic system (HDS) is a potential way to deal with this challenge.
Yue Gao 0002 +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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Knowledge-Embedded Hypergraph Neural Networks
IEEE Transactions on Pattern Analysis and Machine IntelligenceHypergraph Neural Networks (HGNNs) enhance graph-based modeling by representing complex relationships, with applications in brain network analysis, recommendation systems, and computer vision. However, conventional HGNNs often struggle with effective knowledge extraction and discriminative feature representation, leading to performance limitations ...
Yifan Feng +5 more
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Hypergraph Neural Network Hawkes Process
2022 International Joint Conference on Neural Networks (IJCNN), 2022Zi-Hao Cheng, Jian-Wei Liu 0006, Ze Cao
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