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

Hongyun Cai, Vincent W. Zheng, Kevin Chen Chuan Chang

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number8294302
Pages (from-to)1616-1637
Number of pages22
JournalIEEE Transactions on Knowledge and Data Engineering
Volume30
Issue number9
DOIs
StatePublished - Sep 1 2018

Keywords

  • Graph embedding
  • graph analytics
  • graph embedding survey
  • network embedding

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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