Graph Representation Learning: A Survey
Abstract
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.
Cite
@article{arxiv.1909.00958,
title = {Graph Representation Learning: A Survey},
author = {Fenxiao Chen and Yuncheng Wang and Bin Wang and C. -C. Jay Kuo},
journal= {arXiv preprint arXiv:1909.00958},
year = {2020}
}