A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
Open accessAbstract
Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work addresses these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques, and application scenarios.
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Real-world data are often dynamic and evolving, which makes constructing correct and complete knowledge graphs difficult.
“Second, real-world data are often dynamic and evolving, which leads to difficulty in constructing correct and complete KGs [7].”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [7]
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Multidimensional Scaling and Data Clustering
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“[9] provide a survey for the whole Graph Embedding eld.”
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syslrn: learning what to monitor for efficient anomaly detection
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- Authors
- Cai, H., Zheng, V. W., & Chang, K. C.-C.
- Affiliations
- Advanced Digital Sciences Center (SG)
- University of Illinois Urbana-Champaign (US)
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2018-09-01
- Volume
- 30
- Issue
- 9
- Pages
- 1616-1637
- ISSN
- 1041-4347 (ISSN-L)
- 1558-2191
- 2326-3865
- OpenAlex
- W2963224980
- Cited by
- 2,034
- References
- 153
- Citations by year
- 2026: 71 · 2025: 179 · 2024: 229 · 2023: 276 · 2022: 300 · 2021: 369 · 2020: 313 · 2019: 217 · 2018: 78 · 2017: 1
- FWCI
- 137.63
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Transactions on Knowledge and Data Engineering (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Graph Theory and Algorithms
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- graph embedding, graph analysis, node classification, link prediction, low-dimensional representation, computational efficiency, future research directions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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