  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.
