Results 101 to 110 of about 5,998 (189)

A Novel EEG-Based Hypergraph Convolution Network for Depression Detection: Incorporating Unified Brain Network and Multi-Segment Spatiotemporal EEG Features

IEEE Access
Detecting depression from Electroencephalography (EEG) signals remains a challenging task due to the complexity of brain networks and the significant individual differences in neural activity. Traditional models significantly fall short: 1) capturing the
Sudipta Priyadarshinee, Madhumita Panda
doaj   +1 more source

SSRES: A Student Academic Paper Social Recommendation Model Based on a Heterogeneous Graph Approach

Mathematics
In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information.
Yiyang Guo, Zheyu Zhou
doaj   +1 more source

Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation

Knowledge hypergraphs generalize knowledge graphs using hyperedges to connect multiple entities and depict complicated relations. Existing methods either transform hyperedges into an easier-to-handle set of binary relations or view hyperedges as isolated and ignore their adjacencies. Both approaches have information loss and may potentially lead to the
Li, Mengfan, Shi, Xuanhua, Qiao, Chenqi, Zhang, Teng, Jin, Hai   +4 more
openaire   +2 more sources

DPHGNN: A Dual Perspective Hypergraph Neural Networks

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Accepted in SIGKDD'24 -- Research ...
Siddhant Saxena, Shounak Ghatak, Raghu Kolla, Debashis Mukherjee, Tanmoy Chakraborty   +4 more
openaire   +2 more sources

Identifying autism spectrum disorder from multi-modal data with privacy-preserving

npj Mental Health Research
The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models.
Haishuai Wang, Hezi Jing, Jianjun Yang, Chao Liu, Liwei Hu, Guangyu Tao, Ziping Zhao, Ning Shen   +7 more
doaj   +1 more source

A Review of Hypergraph Neural Networks

EAI Endorsed Transactions on e-Learning
In recent years, Graph Neural Networks (GNNs) have seen notable success in fields such as recommendation systems and natural language processing, largely due to the availability of vast amounts of data and powerful computational resources. GNNs are primarily designed to work with graph data that involve pairwise relationships.
openaire   +1 more source

Strong Target Attack on Hypergraph Neural Networks via Label Poisoning and Structure Modification. [PDF]

Entropy (Basel)
Huang J, Sun Q, Zhang N, Zheng M.
europepmc   +1 more source

Higher-order Interaction Matters: Modeling Epidemics via Dynamic Hypergraph Neural Networks. [PDF]

Pac Symp Biocomput
Liu S, Gong S, Feng T, Liu Z, Lau MSY, Jin W.   +5 more
europepmc   +1 more source

Cross-Scale Hypergraph Neural Networks with Inter-Intra Constraints for Mitosis Detection. [PDF]

Sensors (Basel)
Li J, Dong D, Zhan Y, Zhu G, Zhang H, Xie X, Yang L.   +6 more
europepmc   +1 more source

scHyper: reconstructing cell-cell communication through hypergraph neural networks. [PDF]

Brief Bioinform
Li W, Wang H, Zhao J, Xia J, Sun X.
europepmc   +1 more source