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SCIENTIA SINICA Informationis, Volume 47, Issue 8: 980-996(2017) https://doi.org/10.1360/N112017-00145

Network representation learning: an overview

Cunchao TU1,2,3, Cheng YANG1,2,3, Zhiyuan LIU1,2,3,*, Maosong SUN1,2,3
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  • ReceivedJun 30, 2017
  • AcceptedAug 1, 2017
  • PublishedAug 23, 2017

Abstract

Networks are important ways of representing objects and their relationships. A key problem in the study of networks is how to represent the network information properly. With the developments in machine learning, feature learning of network vertices has become an important area of study. Network representation learning algorithms turn network information into dense, low-dimensional real-valued vectors that can be used as inputs for existing machine learning algorithms. For example, the representation of vertices can be fed to a classifier such as a Support Vector Machine (SVM) for vertex classification. In addition, the representations can be used for visualization by taking the representations as points in a Euclidean space. The study of network representation learning has attracted the attention of many researchers. In this article, recent works on network representation learning are introduced and summarized.


Funded by

中国科协青年人才托举计划(2016QNRC001)

国家社会科学基金重大招标项目(13&ZD190)

国家自然科学基金(61572273)

清华大学自主科研项目(201510804 06)

国家重点基础研究发展计划(973)(2014CB340501)


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