Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs.High-dimensional graph data are often in irregular form, which makes them 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 datasets and compare their performance.Finally, potential applications and future directions are presented.

Fenxiao Chen

Yun-Cheng Wang

Bin Wang

C.-C. Jay Kuo

APSIPA Transactions on Signal and Information Processing

Graph representation learning: a survey

This review reviews a wide range of graph embedding techniques with insights and evaluates several stat-of-the-art methods against small and large data sets and compare their performance.

Graph representation learning: a survey

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