A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
It provides a comprehensive overview for researchers and practitioners in machine learning and data science, but is incremental as it synthesizes existing literature without introducing new methods.
This survey tackles the problem of high computational and space costs in graph analytics by reviewing graph embedding techniques that convert graph data into low-dimensional spaces while preserving structural information, summarizing existing methods, taxonomies, applications, and future directions.
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.