A Survey on Network Embedding

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...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 31; H. 5; S. 833 - 852
Hauptverfasser: Cui, Peng, Wang, Xiao, Pei, Jian, Zhu, Wenwu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Computational complexity
Data science
Distributed databases
Embedded systems
Embedding
graph embedding
Image reconstruction
Internet resources
Machine learning
Network analysis
Network embedding
Network topology
Social network services
Task analysis
ISSN:1041-4347, 1558-2191
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Zusammenfassung: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.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2018.2849727