Paper Info
Title
Joint network embedding of network structure and node attributes via deep autoencoder
Abstract
Network embedding aims to learn a low-dimensional vector for each node in networks, which is effective in a variety of applications such as network reconstruction and community detection. However, the majority of the existing network embedding methods merely exploit the network structure and ignore the rich node attributes, which tend to generate sub-optimal network representation. To learn more desired network representation, diverse information of networks should be exploited. In this paper, we develop a novel deep autoencoder framework to fuse topological structure and node attributes named FSADA. We firstly design a multi-layer autoencoder which consists of multiple non-linear functions to capture and preserve the highly non-linear network structure and node attribute information. Particularly, we adopt a pre-processing procedure to pre-process the original information, which can better facilitate to extract the intrinsic correlations between topological structure and node attributes. In addition, we design an enhancement module that combines topology and node attribute similarity to construct pairwise constraints on nodes, and then a graph regularization is introduced into the framework to enhance the representation in the latent space. Our extensive experimental evaluations demonstrate the superior performance of the proposed method.
Year
DOI
Venue
2022
10.1016/j.neucom.2021.10.032
Neurocomputing
Keywords
DocType
Volume
Deep autoencoder,Deep learning,Network analysis,Network embedding,Data mining,Pattern recognition
Journal
468
ISSN
Citations 
PageRank 
0925-2312
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yu Pan101.69
Junhua Zou200.34
Junyang Qiu300.34
Shuaihui Wang402.70
Guyu Hu500.68
Zhisong Pan600.34