2019/09/03 by Fenxiao Chen, Yuncheng Wang, Bin Wang +1 · 1 citation
Computer Science · Mathematics · #cs.LG #cs.SI #stat.ML
paper · pdf · doi:10.1017/atsip.2020.13
published as APSIPA Transactions on Signal and Information Processing 9 (2020) e15
arxiv created 2019/09/03 · arxiv updated 2020/06/03
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 state-of-the-art methods against small and large datasets and compare their performance. Finally, potential applications and future directions are presented.