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

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...

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Bibliographic Details
Main Authors: Shengxiang Hu, Bofeng Zhang, Hehe Lv, Furong Chang, Chenyang Zhou, Liangrui Wu, Guobing Zou
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Entropy
Subjects:
network representation learning
dynamic random walk
feature extraction
Laplacian space optimization
Online Access:https://www.mdpi.com/1099-4300/24/9/1213

Internet

https://www.mdpi.com/1099-4300/24/9/1213

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