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Dynamic Multi-relational Networks Integration and Extended Link Prediction Method

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Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques (IScIDE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9243))

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Abstract

Link prediction is an effective method in complex networks analysis, not only in simple networks but also in dynamic multi-relational ones. How to integrate these multi-relational networks is of great importance to link prediction results. In this paper, we study the integration method of dynamic multi-relational networks and extend the link prediction method which estimates the likelihood of missing links and existent links disappearing in the future. Accordingly, we put forward an algorithm for building dynamic multi-relational weighted networks and an extended link prediction algorithm in integrated dynamic multi-relational networks, which is capable of predicting bi-directional links. We apply our method in a real multi-relational network. The experimental results show that our method can improve the link prediction performance in dynamic multi-relational networks.

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Acknowledgments

Supported by National Natural Science Foundation under Grant (No.61373149, 61472233), Technology Program of Shandong Province under Grant (No. 2012GGX10118,2014GGX101026), Exquisite course project of Shandong Province under Grant (No. 2012BK294, 2013BK402), education research project of Shandong Province under Grant (No. ZK1437B010).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Wenhua Road 88, Jinan, 250014, China

    Hong Wang

  2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Wenhua Road 88, Jinan, 250014, China

    Hong Wang

  3. Department of Earth Sciences, Zhejiang University, Xihu Road, Hangzhou, 310000, China

    Yanshen Sun

Authors
  1. Hong Wang
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  2. Yanshen Sun
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Corresponding author

Correspondence to Hong Wang .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

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Wang, H., Sun, Y. (2015). Dynamic Multi-relational Networks Integration and Extended Link Prediction Method. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_19

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  • DOI: https://doi.org/10.1007/978-3-319-23862-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23861-6

  • Online ISBN: 978-3-319-23862-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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