Graph Representation Learning: A Survey [article]

Fenxiao Chen, Yuncheng Wang, Bin Wang, C.-C. Jay Kuo
2019 arXiv   pre-print
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
more » ... 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.
arXiv:1909.00958v1 fatcat:6wbxy5jjx5ditbiiviwbuqyww4