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Directed hypergraph neural network [PDF]

CoRR, 2020
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. In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we
Loc Hoang Tran, Linh Hoang Tran
core   +4 more sources

Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph [PDF]

Scientific Reports
In 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, Feng Hu, Chunyang Tang, Zhangyu Mei, Chuang Liu   +4 more
doaj   +2 more sources

A lightweight single-view contrastive learning hypergraph neural network for food–microbe–disease association prediction [PDF]

BMC Bioinformatics
Background Identifying potential associations among food, gut microbiota and disease is fundamental for elucidating interaction mechanisms and advancing personalized healthy dietary strategies. While computational methods have been extensively applied to
Jianqiang Hu, Mingyi Hu, Yangxiang Wu, Songyao Mu, Dahao Huang, Baolong Wang, Yuchen Gao, Shixin Gu, Jinlin Zhu   +8 more
doaj   +2 more sources

Hierarchical Network Organization and Dynamic Perturbation Propagation in Autism Spectrum Disorder: An Integrative Machine Learning and Hypergraph Analysis Reveals Super-Hub Genes and Therapeutic Targets [PDF]

Biomedicines
Background/Objectives: Autism spectrum disorder (ASD) exhibits remarkable genetic heterogeneity involving hundreds of risk genes; however, the mechanism by which these genes organize within biological networks to contribute to disease pathogenesis ...
Larissa Margareta Batrancea, Ömer Akgüller, Mehmet Ali Balcı, Lucian Gaban   +3 more
doaj   +2 more sources

Recent Advances in Hypergraph Neural Networks

Journal of the Operations Research Society of China
Abstract The growing interest in hypergraph neural networks (HGNNs) is driven by their capacity to capture the complex relationships and patterns within hypergraph structured data across various domains, including computer vision, complex networks, and natural language processing.
Xin-Jian Xu, Xu Xin-Jian
exaly   +3 more sources

HyperGraph-based capsule temporal memory network for efficient and explainable diabetic retinopathy detection in retinal imaging [PDF]

Scientific Reports
Diabetic retinopathy (DR) is a chronic complication of diabetes in which the retinal damage may cause vision impairment or blindness if left untreated. The challenges in DR detection are mostly due to the morphological variations of retinal lesions, e.g.,
Mishmala Sushith, N. Malligeswari, M. Anlin Sahaya Infant Tinu, M. Jaiganesh   +3 more
doaj   +2 more sources

Adaptive dynamic hypergraph learning for ingredient aware food recommendation [PDF]

Scientific Reports
Food recommendation systems face fundamental challenges in modeling the complex, compositional relationships among users, foods, and ingredients. Traditional collaborative filtering and Graph Neural Networks rely on pairwise connections that oversimplify
Yazeed Alkhrijah, Abbas N. Talib, Narinderjit Singh Sawaran Singh, Ashraf Abed Hussein   +3 more
doaj   +2 more sources

Hypergraph-based contrastive learning for enhanced fraud detection [PDF]

Frontiers in Artificial Intelligence
The proliferation of digital platforms has enabled fraudsters to deploy sophisticated camouflage techniques, such as multi-hop collaborative attacks, to evade detection.
Qinhong Wang, Yiming Shen, Husheng Dong
doaj   +2 more sources

Multi-site Hyper-graph Convolutional Neural Networks and Application [PDF]

Jisuanji kexue, 2022
Recently,the exploitation of graph neural networks for neurological brain disorder diagnosis has attracted much attention.However,the graphs used in the existing studies are usually based on the pairwise connections of different nodes,and thus cannot ...
ZHOU Hai-yu, ZHANG Dao-qiang
doaj   +1 more source

Model of the malicious traffic classification based on hypergraph neural network

网络与信息安全学报, 2023
As the use and reliance on networks continue to grow, the prevalence of malicious network traffic poses a significant challenge in the field of network security.Cyber attackers constantly seek new ways to infiltrate systems, steal data, and disrupt ...
Wenbo ZHAO, Zitong MA, Zhe YANG
doaj   +3 more sources