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Link prediction in social networks: the state-of-the-art

期刊

SCIENCE CHINA-INFORMATION SCIENCES
卷 58, 期 1, 页码 -

出版社

SCIENCE PRESS
DOI: 10.1007/s11432-014-5237-y

关键词

social network; link prediction; dynamic network; similarity metric; learning model

资金

  1. National Key Basic Research and Development Program of China [2014CB340702]
  2. National Natural Science Foundation of China [61170071, 91318301, 61321491, 61472077]
  3. China Postdoctoral Science Foundation [2014M560378]
  4. Foundation of the State Key Laboratory of Software Engineering (SKLSE)

向作者/读者索取更多资源

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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