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Development of game theoretic hypergraph based autoencoder scheme for multiple objects tracking and anomaly detection for surveillance videos. [PDF]
Sci RepSrinivasan P +4 more
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A hypergraph convolution-based intelligent healthcare platform for aging population management. [PDF]
Front Public HealthLi W, Hou B, Wang XX.
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Hypergraph Neural Network for Skeleton-Based Action Recognition
IEEE Transactions on Image Processing, 2021Recently, skeleton-based human action recognition has attracted a lot of research attention in the field of computer vision. Graph convolutional networks (GCNs), which model the human body skeletons as spatial-temporal graphs, have shown excellent results. However, the existing methods only focus on the local physical connection between the joints, and
Xiaoke Hao +4 more
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Hypergraph Transformer Neural Networks
ACM Transactions on Knowledge Discovery from Data, 2023Graph 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 +4 more
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Kernelized Hypergraph Neural Networks
IEEE Transactions on Pattern Analysis and Machine IntelligenceYifan Feng +4 more
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Hypergraph Structure Learning for Hypergraph Neural Networks
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022Hypergraphs 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 +5 more
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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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