Results 91 to 100 of about 2,491 (218)
Hypergraph Learning: From Algorithms to Applications
Graphs are a general language for describing and modeling interconnected systems. To learn graph data, Graph Neural Networks (GNNs) have been introduced.
Saifuddin, Khaled Mohammed
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Directed hypergraph neural network
, 2022 To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph.
Tran, Linh Hoang, Tran, Loc Hoang
core
Definition and Computation of Tensor‐Based Generalized Function Composition
Numerical Linear Algebra with Applications, Volume 33, Issue 1, February 2026.ABSTRACT Functions are fundamental to mathematics as they offer a structured and analytical framework to express relations between variables. While scalar and matrix‐based functions are well‐established, higher‐order tensor‐based functions have not been as extensively explored.
Remy Boyer
wiley +1 more source
Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph
Scientific ReportsIn recent years, hypergraph neural networks have achieved remarkable success in tasks such as node classification, link prediction, and graph classification, thanks to their powerful computational capabilities.
Libing Bai +4 more
doaj +1 more source
IEEE Open Journal of the Communications SocietyGenerative AI is revolutionizing Software Engineering (SE), as both engineers and academics embrace this technology in their work. To better leverage this technology for software generation, it is essential to propose effective IoT software defect ...
Nan Wang +8 more
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Lightweight Hybrid Wafer Defect Pattern Network Based on Feedforward Efficient Attention
CAAI Transactions on Intelligence Technology, Volume 11, Issue 1, Page 149-166, February 2026.ABSTRACT With the increase of semiconductor integration density, in order to cope with the increase of wafer defect complexity and types, especially the low recognition accuracy of overlapping mixed defects and unknown wafer defects, this study proposes a lightweight model for wafer defect detection called LightWMNet.
Zhiqiang Hu, Yiquan Wu
wiley +1 more source
Transactions on Emerging Telecommunications Technologies, Volume 37, Issue 1, January 2026.IntelliMetro‐Hybrid is an intelligent fusion framework that integrates machine learning (ML) and deep learning (DL) for real‐time anomaly detection and economic optimization in smart metro systems. The model combines tree‐based feature extraction (Random Forest, XG Boost) with a deep neural classifier to effectively handle imbalanced, heterogeneous ...
Sijin Peng +6 more
wiley +1 more source
ComplexityStock ranking prediction is an effective method for achieving a high investment return and plays a crucial role in investment decisions. However, previous studies have overlooked the interconnections among stocks or have solely relied on predefined ...
Jianlong Hao +4 more
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Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence Recommendation
IEEE AccessExisting multi-behavior sequential recommendation methods obtain users’ interest preferences by analyzing their historical multi-behavior information to uncover users’ potential intentions in multi-behavior sequential recommendation ...
Sitong Yan +3 more
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Hypergraph Label Propagation Network
, 2020 In recent years, with the explosion of information on the Internet, there has been a large amount of data produced, and analyzing these data is useful and has been widely employed in real world applications.
Wang, Nan +6 more
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