Network Representation Learning: From Traditional Feature Learning to Deep Learning

• Published in: IEEE access 2020, Vol.8, p.205600-205617
• Main Authors: Sun, Ke, Wang, Lei, Xu, Bo, Zhao, Wenhong, Teng, Shyh Wei, Xia, Feng
• Format: Article
• Language: English
• Subjects: Acoustics; Data processing; Deep learning; graph analytics; Laplace equations; Manifolds; network representation learning; Principal component analysis; Recommender systems; Representations; Signal processing algorithms; Social networks; Spatial data; Sun; Task analysis; Traditional feature learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3037118

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.