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Link prediction in social networks: the state-of-the-art
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  • 作者：Peng Wang (1) (3)
    BaoWen Xu (1) (2) (3)
    YuRong Wu (1)
    XiaoYu Zhou (1)

    1. School of Computer Science and Engineering     ; Southeast University ; Nanjing ; 210096 ; China
    3. State Key Laboratory of Software Engineering     ; Wuhan University ; Wuhan ; 430072 ; China
    2. State Key Laboratory for Novel Software Technology     ; Nanjing University ; Nanjing ; 210023 ; China
  • 关键词：social network ; link prediction ; dynamic network ; similarity metric ; learning model ; 绀句氦缃戠粶 ; 閾炬帴棰勬祴 ; 鍔ㄦ€佺綉缁?/li> 鐩镐技搴﹂噺 ; 瀛︿範妯″瀷 ; 011101
  • 刊名：SCIENCE CHINA Information Sciences
  • 出版年：2015
  • 出版时间：January 2015
  • 年：2015
  • 卷：58
  • 期：1
  • 页码：1-38
  • 全文大小：839 KB
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  • 刊物类别：Computer Science
  • 刊物主题：Chinese Library of Science
    Information Systems and Communication Service
  • 出版者：Science China Press, co-published with Springer
  • ISSN：1869-1919
文摘
In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.

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