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Network Representation Learning: From Traditional Feature Learning to Deep Learning
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DOI：10.1109/ACCESS.2020.3037118.png)
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摘要
En 中文
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.
Keyword:
Traditional feature learning
network representation learning
deep learning
graph analytics
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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