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Open Access

A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure

School of Computer Science and Engineering, and also with MOE Key Laboratory of Computer Network and Information Integration, Southeast University, Nanjing 211100, China.
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Network embedding is a very important task to represent the high-dimensional network in a low-dimensional vector space, which aims to capture and preserve the network structure. Most existing network embedding methods are based on shallow models. However, actual network structures are complicated which means shallow models cannot obtain the high-dimensional nonlinear features of the network well. The recently proposed unsupervised deep learning models ignore the labels information. To address these challenges, in this paper, we propose an effective network embedding method of Structural Labeled Locally Deep Nonlinear Embedding (SLLDNE). SLLDNE is designed to obtain highly nonlinear features through utilizing deep neural network while preserving the label information of the nodes by using a semi-supervised classifier component to improve the ability of discriminations. Moreover, we exploit linear reconstruction of neighborhood nodes to enable the model to get more structural information. The experimental results of vertex classification on two real-world network datasets demonstrate that SLLDNE outperforms the other state-of-the-art methods.


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Big Data Mining and Analytics
Pages 205-216
Cite this article:
Wu W, Yu Z, He J. A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure. Big Data Mining and Analytics, 2019, 2(3): 205-216.








Web of Science






Received: 22 November 2018
Accepted: 13 February 2019
Published: 04 April 2019
© The author(s) 2019