A Survey on Network Embedding
Open accessAbstract
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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- Authors
- Cui, P., Wang, X., Pei, J., & Zhu, W.
- Affiliations
- Tsinghua University (CN)
- Simon Fraser University (CA)
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2019-05-01
- Volume
- 31
- Issue
- 5
- Pages
- 833-852
- ISSN
- 1041-4347 (ISSN-L)
- 1558-2191
- 2326-3865
- OpenAlex
- W2962975498
- Cited by
- 1,274
- References
- 90
- Citations by year
- 2026: 38 · 2025: 80 · 2024: 110 · 2023: 185 · 2022: 190 · 2021: 258 · 2020: 224 · 2019: 145 · 2018: 43
- FWCI
- 94.54
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Knowledge and Data Engineering (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Program on Key Basic Research Project (2015CB352300)
- National Natural Science Foundation of China (U1611461, 61772304, 61521002, 61531006, 61702296)
- research fund of the Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology
- Young Elite Scientist Sponsorship Program
- Natural Sciences and Engineering Research Council of Canada
- Canada Research Chairs
- NSERC Strategic Grant program
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Graph Theory and Algorithms
- Keywords
- graph embedding, node representation learning, structure preservation, property preservation, side information, information retention, network datasets, network analysis
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