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Link prediction based on path entropy

Author

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  • Xu, Zhongqi
  • Pu, Cunlai
  • Yang, Jian

Abstract

Information theory has been taken as a prospective tool for quantifying the complexity of complex networks. In this paper, first we study the information entropy or uncertainty of a path using the information theory. After that, we apply the path entropy to the link prediction problem in real-world networks. Specifically, we propose a new similarity index, namely Path Entropy (PE) index, which considers the information entropies of shortest paths between node pairs with penalization to long paths. Empirical experiments demonstrate that PE index outperforms the mainstream of link predictors.

Suggested Citation

  • Xu, Zhongqi & Pu, Cunlai & Yang, Jian, 2016. "Link prediction based on path entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 294-301.
    • Handle: RePEc:eee:phsmap:v:456:y:2016:i:c:p:294-301
    DOI: 10.1016/j.physa.2016.03.091
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    Cited by:

    1. 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).
    2. 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.
    3. Yin, Likang & Zheng, Haoyang & Bian, Tian & Deng, Yong, 2017. "An evidential link prediction method and link predictability based on Shannon entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 699-712.
    4. Wu, Jiehua & Shen, Jing & Zhou, Bei & Zhang, Xiayan & Huang, Bohuai, 2019. "General link prediction with influential node identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 996-1007.
    5. 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.

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