APSIPA Transactions on Signal and Information Processing > Vol 9 > Issue 1

Graph representation learning: a survey

Fenxiao Chen, University of Southern California, USA, fenxiaoc@usc.edu , Yun-Cheng Wang, University of Southern California, USA, Bin Wang, University of Southern California, USA, C.-C. Jay Kuo, University of Southern California, USA
 
Suggested Citation
Fenxiao Chen, Yun-Cheng Wang, Bin Wang and C.-C. Jay Kuo (2020), "Graph representation learning: a survey", APSIPA Transactions on Signal and Information Processing: Vol. 9: No. 1, e15. http://dx.doi.org/10.1017/ATSIP.2020.13

Publication Date: 28 May 2020
© 2020 Fenxiao Chen, Yun-Cheng Wang, Bin Wang and C.-C. Jay Kuo
 
Subjects
 
Keywords
Graph embeddingrepresentation learningmachine learningartificial intelligencedata mining
 

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This is published under the terms of the Creative Commons Attribution licence.

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In this article:
I. INTRODUCTION 
II. DEFINITION AND PRELIMINARIES 
III. Graph embedding methods 
IV. COMPARISON OF DIFFERENT METHODS and APPLICATIONS 
V. EVALUATION 
VI. EMERGING APPLICATIONS 
VII. FUTURE RESEARCH DIRECTIONS 
VIII. CONCLUSION 

Abstract

Research on graph representation learning has received great attention in recent years since most data in real-world applications come in the form of graphs. High-dimensional graph data are often in irregular forms. They are more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several stat-of-the-art methods against small and large data sets and compare their performance. Finally, potential applications and future directions are presented.

DOI:10.1017/ATSIP.2020.13