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Link Prediction in Complex Networks: A Mutual Information Perspective

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  • Fei Tan
  • Yongxiang Xia
  • Boyao Zhu

Abstract

Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.

Suggested Citation

  • Fei Tan & Yongxiang Xia & Boyao Zhu, 2014. "Link Prediction in Complex Networks: A Mutual Information Perspective," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-8, September.
    • Handle: RePEc:plo:pone00:0107056
    DOI: 10.1371/journal.pone.0107056
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Xuejun & Pang, Wenbo & Xia, Yongxiang, 2018. "An intermediary probability model for link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 902-912.
    2. Kumar, Ajay & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction techniques, applications, and performance: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    3. Shakibian, Hadi & Charkari, Nasrollah Moghadam, 2018. "Statistical similarity measures for link prediction in heterogeneous complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 248-263.
    4. Namika Makhija & Shashank Mouli Satapathy, 2021. "Community detection in dynamic networks: a comprehensive and comparative review using external and internal criteria," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(2), pages 217-230, April.
    5. Huaylla, Claudia A. & Kuperman, Marcelo N. & Garibaldi, Lucas A., 2024. "Comparison of two statistical measures of complexity applied to ecological bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    6. Charikhi, Mourad, 2024. "Association of the PageRank algorithm with similarity-based methods for link prediction in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    7. Yao, Yabing & Zhang, Ruisheng & Yang, Fan & Tang, Jianxin & Yuan, Yongna & Hu, Rongjing, 2018. "Link prediction in complex networks based on the interactions among paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 52-67.
    8. Chi, Kuo & Qu, Hui & Yin, Guisheng, 2022. "Link prediction for existing links in dynamic networks based on the attraction force," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    9. Pei, Panpan & Liu, Bo & Jiao, Licheng, 2017. "Link prediction in complex networks based on an information allocation index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 1-11.

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