期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 31, 期 5, 页码 833-852出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2018.2849727
关键词
Network embedding; graph embedding; network analysis; data science
类别
资金
- National Program on Key Basic Research Project [2015CB352300]
- National Natural Science Foundation of China [U1611461, 61772304, 61521002, 61531006, 61702296]
- Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology
- Young Elite Scientist Sponsorship Program, CAST
- NSERC Discovery Grant program
- Canada Research Chair program
- NSERC Strategic Grant program
Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure-and property-preserving network embedding methods, the network embedding methods with side information, and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.
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