PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning

Hypergraphs are powerful tools for modeling complex interactions across various domains, including biomedicine. However, learning meaningful node representations from hypergraphs remains a challenge. Existing supervised methods often lack generalizability, thereby limiting their real-world applicati...

Descripción completa

Detalles Bibliográficos
Autores principales: Deng, Yihe, Zhang, Ruochi, Xu, Pan, Ma, Jian, Gu, Quanquan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592843/
https://www.ncbi.nlm.nih.gov/pubmed/37873233
http://dx.doi.org/10.1101/2023.10.01.560404

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592843/
https://www.ncbi.nlm.nih.gov/pubmed/37873233
http://dx.doi.org/10.1101/2023.10.01.560404

Ejemplares similares