Optimisation of sparse deep autoencoders for dynamic network embedding

Optimisation of sparse deep autoencoders for dynamic network embedding

Abstract Network embedding (NE) tries to learn the potential properties of complex networks represented in a low‐dimensional feature space. However, the existing deep learning‐based NE methods are time‐consuming as they need to train a dense architecture for deep neural networks with extensive unkno...

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Bibliographic Details
Main Authors: Huimei Tang, Yutao Zhang, Lijia Ma, Qiuzhen Lin, Liping Huang, Jianqiang Li, Maoguo Gong
Format: Article
Language:English
Published: Wiley 2024-12-01
Series:CAAI Transactions on Intelligence Technology
Subjects:
deep autoencoder
dynamic networks
low‐dimensional feature space
network embedding
sparse structure
Online Access:https://doi.org/10.1049/cit2.12367
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