Unsupervised Embedding Learning for Large-Scale Heterogeneous Networks Based on Metapath Graph Sampling

How to learn the embedding vectors of nodes in unsupervised large-scale heterogeneous networks is a key problem in heterogeneous network embedding research. This paper proposes an unsupervised embedding learning model, named LHGI (Large-scale Heterogeneous Graph Infomax). LHGI adopts the subgraph sa...

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Detalles Bibliográficos
Autores principales: Zhong, Hongwei, Wang, Mingyang, Zhang, Xinyue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955212/
https://www.ncbi.nlm.nih.gov/pubmed/36832662
http://dx.doi.org/10.3390/e25020297

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955212/
https://www.ncbi.nlm.nih.gov/pubmed/36832662
http://dx.doi.org/10.3390/e25020297

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