Results 151 to 160 of about 2,491 (218)

Non-Adaptive Learning of Random Hypergraphs with Queries

2025 IEEE International Symposium on Information Theory (ISIT)
We study the problem of learning a hidden hypergraph $G=(V,E)$ by making a single batch of queries (non-adaptively). We consider the hyperedge detection model, in which every query must be of the form: ``Does this set $S\subseteq V$ contain at least one full hyperedge?'' In this model, it is known that there is no algorithm that allows to non ...
Bethany Austhof, Lev Reyzin, Erasmo Tani
openaire   +2 more sources

ERP Insights and Truncated SVD in Conjunction with Dual-Tree Complex Wavelet Transform and Multi-View Hypergraph Neural Networks for Cognitive Distortion Analysis

International Journal of Computational Intelligence Systems
Multi-modal EEG data analysis requires sophisticated methods for accurate prediction in the critical area of cognitive depression study in neuroscience.
N. Banupriya, Neelakandan Subramani, M. Prakash, S. Velmurugan   +3 more
doaj   +1 more source

MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association. [PDF]

Front Genet, 2020
Wu Q, Wang Y, Gao Z, Ni J, Zheng C.
europepmc   +1 more source

Interactive, Enhanced Dual Hypergraph Model for Explainable Contrastive Learning Recommendation

In recent years, it has become a hot topic to combine graph neural networks with contrastive learning, a method which has been applied not only in recommendation tasks but has also achieved impressive results in many fields, such as text processing and ...
Xiang Wan, Jiwei Hu, Lingyu Yan, Donghua Liu, Xinyun Wu, Jin Li, Rong Gao   +6 more
core   +1 more source

Traffic flow prediction via dynamic hypergraph learning. [PDF]

PLoS One
Wei S, Yang Y, Wang C.
europepmc   +1 more source

A graph clustering algorithm with hypergraph learning and a core-attachment strategy for protein complex identification. [PDF]

Front Genet
Wang J, Yang X, Yang P, Yang J, Miao Y.
europepmc   +1 more source

HGCLMDA: Predicting mRNA–Drug Sensitivity Associations via Hypergraph Contrastive Learning

The identification of drug sensitivity to mRNA interactions is crucial for drug development and disease treatment, but traditional experimental methods for verifying mRNA–drug sensitivity associations are labor-intensive and time-consuming. In this study,
Lei Deng (22477), Xiaowen Hu (207580), Yihan Dong (16945478), Jiaxuan Zhang (8614650)   +3 more
core   +1 more source

Leveraging unified multi-view hypergraph learning for neurodevelopmental disorders diagnosis. [PDF]

Front Med (Lausanne)
Han X, Li J.
europepmc   +1 more source

Hypergraph Learning with Hyperedge Gating and Multiscale Topology Feature Learning for Predicting Disease-Related circRNAs. [PDF]

ACS Omega
Xuan P, Zheng R, Cui H, Sun F, Wang F, Zhang T.   +5 more
europepmc   +1 more source

Hypergraph Joint Representation Learning for Hypervertices and Hyperedges via Cross Expansion

Hypergraph captures high-order information in structured data and obtains much attention in machine learning and data mining. Existing approaches mainly learn representations for hypervertices by transforming a hypergraph to a standard graph, or learn ...
Yan, Yuguang, Cai, Ruichu, Chen, Yuanlin, Wang, Shibo, Wu, Hanrui   +4 more
core   +1 more source