186 / 2016-12-20 13:54:30
Local Community Detection Algorithm Based on Links And Content
Social network,Local Community Detection,Links and Content,Seed Set
Draft Accepted
Cuijuan Wang / School of Computer Science and Engineering, Beihang University
Wenzhong Tang / School of Computer Science and Engineering, Beihang University
Yanyang Wang / School of Aeronautic Science and Engineering, Beihang University
Jing Fang / National Computer Network Emergency Response Technical Team/Coordination Center of China
Shan Yao / National Computer Network Emergency Response Technical Team/Coordination Center of China
Community detection is an important field in research of social networks. There exist a lot of algorithms which most of them are based on the density of connections between groups of nodes. On the one hand, the error and lack of links may lead to great impact on the result of community detection. On the other hand, there are users with deep relation but without much communication, so the density of connections can’t represent weather the users belong to the same community or not. With the network becoming more and more complicated, the traditional global method will cost much time and space. In this paper, we proposed a local method based on links and content, and the method focuses on particular users’ communities. The test results on Enron email dataset have shown the superior performance of our proposed method in community detection.
Important Date
  • Conference Date

    Mar 25

    2017

    to

    Mar 26

    2017

  • Nov 10 2016

    Draft paper submission deadline

  • Nov 20 2016

    Draft Paper Acceptance Notification

  • Nov 30 2016

    Final Paper Deadline

  • Mar 26 2017

    Registration deadline

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IEEE Beijing Section
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