Graph Representation Learning: A Survey
This is an incremental survey that provides a comprehensive overview for researchers and practitioners working with graph data in real-world applications.
The paper surveys graph representation learning techniques, which tackle the challenge of analyzing high-dimensional, irregular graph data by converting it into low-dimensional vector representations while preserving intrinsic properties, and it evaluates state-of-the-art methods on datasets to compare their performance.
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.