- Title
- Network representation learning: From traditional feature learning to deep learning
- Creator
- Sun, Ke; Wang, Lei; Xu, Bo; Zhao, Wenhong; Teng, Shyh; Xia, Feng
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176368
- Identifier
- vital:15129
- Identifier
-
https://doi.org/10.1109/ACCESS.2020.3037118
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 8, no. (2020), p. 205600-205617
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 IEEE
- Rights
- Open Access
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Subject
- 08 Information and Computing Sciences; 09 Engineering; 10 Technology; Deep learning; Graph analytics; Network representation learning; Traditional feature learning
- Full Text
- Reviewed
- Funder
- This work was supported in part by the Zhejiang Provincial Fundamental Public Welfare Research Program under Grant LGG18E050025.
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