IEEE Transactions on Knowledge and Data Engineering· 2018Q1· Review
A Survey on Network Embedding
- 1,284citations
- Q1SCImago
- 2018year
Short summary
This survey categorizes and reviews recent developments in network embedding methods, which represent nodes in a network as low-dimensional vectors while preserving network structure.
AI-generated from the title and abstract; the full text is not read.
Key points
- Network embedding represents network nodes in low-dimensional space while preserving network structure.
- Methods are categorized by structure/property preservation, inclusion of side information, and advanced information preservation.
- The survey covers evaluation approaches, datasets, and software for network embedding.
- Future research directions and system-building frameworks are discussed.
AI-generated from the title and abstract; the full text is not read.
Abstract
Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure- and property-preserving network embedding methods, the network embedding methods with side information, and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.
The authors' abstract, as published at the source. IEEE Transactions on Knowledge and Data Engineering, 2018 · DOI ↗
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Field: Statistical and Nonlinear Physics
Statistical and Nonlinear PhysicsPhysics and Astronomy