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Social Link Prediction Based on the Users’ Information Transfer

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Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9865))

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Abstract

Link prediction is one of the hotspots in social network analysis. However, traditional prediction method based on node similarity of network topology does not take the characteristics of user-generated content into account. In this article, the user-generated content based on traditional link prediction method is introduced to predict new user relationships through common neighbors in the social networks. This method is an information-theoretic measure with a predictive interpretation that directly quantifies the strength of the effect of one user’s content on another’s. Experimental results show that content transfer combined with topology is more consistent with the real social network, which has better performance in link prediction.

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Author information

Authors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, China

    Chen Yunfang, Wang Tongli & Zhang Wei

Authors
  1. Chen Yunfang
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  2. Wang Tongli
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  3. Zhang Wei
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Corresponding author

Correspondence to Zhang Wei .

Editor information

Editors and Affiliations

  1. University of Tsukuba , Tsukuba, Japan

    Atsuyuki Morishima

  2. East China Normal University , Shanghai, China

    Rong Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Wenjie Zhang

  4. The University of New South Wales, School of Computer Science and Engineering, Sydney, Australia

    Lijun Chang

  5. Advanced Digital Sciences Center, Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center, Fusionopolies Way, Singapore

    Tom Z. J Fu

  6. Hyundai Motor Tower, Pivotal Inc 15/F, Hyundai Motor Tower, Chaoyang, China

    Kuien Liu

  7. Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center , Singapore, Singapore

    Xiaoyan Yang

  8. South China Normal University, School of Computer Science, Guangzhou, China

    Jia Zhu

  9. Department of Computer Science, Hong Kong Baptist University Department of Computer Science, Kowloon Tong, Hong Kong

    Zhiwei Zhang

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© 2016 Springer International Publishing Switzerland

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Yunfang, C., Tongli, W., Wei, Z. (2016). Social Link Prediction Based on the Users’ Information Transfer. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-45835-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45834-2

  • Online ISBN: 978-3-319-45835-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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