A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure
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 wh...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2019-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2019.9020004 |
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| author | Wenmao Wu Zhizhou Yu Jieyue He |
| author_facet | Wenmao Wu Zhizhou Yu Jieyue He |
| author_sort | Wenmao Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a670ac7f9d984ab1b80ccd97db496e83 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2019-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-a670ac7f9d984ab1b80ccd97db496e832025-02-02T23:47:57ZengTsinghua University PressBig Data Mining and Analytics2096-06542019-09-012320521610.26599/BDMA.2019.9020004A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood StructureWenmao Wu0Zhizhou Yu1Jieyue He2<institution content-type="dept">School of Computer Science and Engineering, and also with MOE Key Laboratory of Computer Network and Information Integration</institution>, <institution>Southeast University</institution>, <city>Nanjing</city> <postal-code>211100</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Engineering, and also with MOE Key Laboratory of Computer Network and Information Integration</institution>, <institution>Southeast University</institution>, <city>Nanjing</city> <postal-code>211100</postal-code>, <country>China</country>.<institution content-type="dept">School of Computer Science and Engineering, and also with MOE Key Laboratory of Computer Network and Information Integration</institution>, <institution>Southeast University</institution>, <city>Nanjing</city> <postal-code>211100</postal-code>, <country>China</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2019.9020004network embeddingdeep learningnetwork analysis |
| spellingShingle | Wenmao Wu Zhizhou Yu Jieyue He A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure Big Data Mining and Analytics network embedding deep learning network analysis |
| title | A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure |
| title_full | A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure |
| title_fullStr | A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure |
| title_full_unstemmed | A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure |
| title_short | A Semi-Supervised Deep Network Embedding Approach Based on the Neighborhood Structure |
| title_sort | semi supervised deep network embedding approach based on the neighborhood structure |
| topic | network embedding deep learning network analysis |
| url | https://www.sciopen.com/article/10.26599/BDMA.2019.9020004 |
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