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

TLDR

Graph data appears in many real‑world scenarios, and while effective graph analytics can reveal deep insights, it often incurs high computational and storage costs; graph embedding offers an efficient solution by projecting graphs into low‑dimensional spaces that preserve structural information. In this survey, we conduct a comprehensive review of the literature in graph embedding. The survey first defines graph embedding formally, then proposes two taxonomies that categorize existing methods by the challenges they address in different problem settings. We conclude by outlining the applications enabled by graph embedding and proposing four future research directions focused on computational efficiency, problem settings, techniques, and application scenarios.

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 maximumly 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 addresses 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.