A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
Top Cited Papers
- 19 February 2018
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Knowledge and Data Engineering
- Vol. 30 (9), 1616-1637
- https://doi.org/10.1109/tkde.2018.2807452
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.Keywords
This publication has 83 references indexed in Scilit:
- Representation Learning: A Review and New PerspectivesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
- Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVDBehavior Research Methods, 2012
- DBpedia - A crystallization point for the Web of DataJournal of Web Semantics, 2009
- Fast linear algebra is stableNumerische Mathematik, 2007
- The Isomap Algorithm and Topological StabilityScience, 2002
- Nonlinear Dimensionality Reduction by Locally Linear EmbeddingScience, 2000
- Long Short-Term MemoryNeural Computation, 1997
- Semidefinite ProgrammingSIAM Review, 1996
- Eigenvalues of the Laplacian of a graph∗Linear and Multilinear Algebra, 1985
- Singular value decomposition and least squares solutionsNumerische Mathematik, 1970