Research Area:  Machine Learning
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.
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Author(s) Name:  Fenxiao Chen, Yun-Cheng Wang , Bin Wang and C.-C. Jay Kuo
Journal name:  APSIPA Transactions on Signal and Information Processing
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Publisher name:  Cambridge University Press
DOI:  10.1017/ATSIP.2020.13
Volume Information:  Volume 9