Network Representation Learning: From Traditional Feature Learning to Deep Learning

• URL: http://arxiv.org/abs/2103.04339v1
• Date: Sun, 7 Mar 2021 12:31:33 GMT
• Title: Network Representation Learning: From Traditional Feature Learning to Deep Learning
• Authors: Ke Sun, Lei Wang, Bo Xu, Wenhong Zhao, Shyh Wei Teng, Feng Xia
• Abstract summary: 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.
• Score: 13.18795412068976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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