Graph representation learning: a survey

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 la...

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Published inAPSIPA transactions on signal and information processing Vol. 9; no. 1
Main Authors Chen, Fenxiao, Wang, Yun-Cheng, Wang, Bin, Kuo, C.-C. Jay
Format Journal Article
LanguageEnglish
Published Hanover Now Publishers Inc 01.01.2020
Subjects
Audio data
Embedding
Graph representations
Graphical representations
Lattices
Learning
Libraries
Multimedia
Noise
Performance evaluation
Probability distribution
Social networks
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Summary: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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ISSN:2048-7703
2048-7703
DOI:10.1017/ATSIP.2020.13