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Hoang VT, Jeon HJ, You ES, Yoon Y, Jung S, Lee OJ. Graph Representation Learning and Its Applications: A Survey. SENSORS (BASEL, SWITZERLAND) 2023; 23:4168. [PMID: 37112507 PMCID: PMC10144941 DOI: 10.3390/s23084168] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/16/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional vectors while preserving graph structure and entity relationships. Over the decades, many models have been proposed for graph representation learning. This paper aims to show a comprehensive picture of graph representation learning models, including traditional and state-of-the-art models on various graphs in different geometric spaces. First, we begin with five types of graph embedding models: graph kernels, matrix factorization models, shallow models, deep-learning models, and non-Euclidean models. In addition, we also discuss graph transformer models and Gaussian embedding models. Second, we present practical applications of graph embedding models, from constructing graphs for specific domains to applying models to solve tasks. Finally, we discuss challenges for existing models and future research directions in detail. As a result, this paper provides a structured overview of the diversity of graph embedding models.
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Affiliation(s)
- Van Thuy Hoang
- Department of Artificial Intelligence, The Catholic University of Korea, 43, Jibong-ro, Bucheon-si 14662, Gyeonggi-do, Republic of Korea; (V.T.H.); (E.-S.Y.)
| | - Hyeon-Ju Jeon
- Data Assimilation Group, Korea Institute of Atmospheric Prediction Systems (KIAPS), 35, Boramae-ro 5-gil, Dongjak-gu, Seoul 07071, Republic of Korea;
| | - Eun-Soon You
- Department of Artificial Intelligence, The Catholic University of Korea, 43, Jibong-ro, Bucheon-si 14662, Gyeonggi-do, Republic of Korea; (V.T.H.); (E.-S.Y.)
| | - Yoewon Yoon
- Department of Social Welfare, Dongguk University, 30, Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea;
| | - Sungyeop Jung
- Semiconductor Devices and Circuits Laboratory, Advanced Institute of Convergence Technology (AICT), Seoul National University, 145, Gwanggyo-ro, Yeongtong-gu, Suwon-si 16229, Gyeonggi-do, Republic of Korea;
| | - O-Joun Lee
- Department of Artificial Intelligence, The Catholic University of Korea, 43, Jibong-ro, Bucheon-si 14662, Gyeonggi-do, Republic of Korea; (V.T.H.); (E.-S.Y.)
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