A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications

TL;DR

This survey comprehensively reviews graph embedding techniques, challenges, and applications, highlighting their importance in efficient graph analytics and outlining future research directions.

Contribution

It provides a detailed taxonomy of graph embedding methods, formal definitions, and a summary of applications and future research directions.

Findings

Taxonomies of graph embedding methods and challenges

Summary of applications like node classification and link prediction

Future directions in efficiency, problem settings, and techniques

Abstract

Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximally preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.