Improving Network Representation Learning via Dynamic Random Walk, Self-Attention and Vertex Attributes-Driven Laplacian Space Optimization

Network data analysis is a crucial method for mining complicated object interactions. In recent years, random walk and neural-language-model-based network representation learning (NRL) approaches have been widely used for network data analysis. However, these NRL approaches suffer from the following...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Shengxiang, Zhang, Bofeng, Lv, Hehe, Chang, Furong, Zhou, Chenyang, Wu, Liangrui, Zou, Guobing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498033/
https://www.ncbi.nlm.nih.gov/pubmed/36141099
http://dx.doi.org/10.3390/e24091213

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498033/
https://www.ncbi.nlm.nih.gov/pubmed/36141099
http://dx.doi.org/10.3390/e24091213

Ejemplares similares