Network Representation Learning: From Traditional Feature Learning to Deep Learning
It provides a comprehensive overview for researchers and practitioners in fields like social networks and bioinformatics, but it is incremental as a survey paper.
This survey investigates network representation learning (NRL) techniques, covering traditional feature learning and deep learning-based models, and analyzes their relationships and progress to address the challenge of applying deep learning to graph-structured data.
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.