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An evidential link prediction method and link predictability based on Shannon entropy

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  • Yin, Likang
  • Zheng, Haoyang
  • Bian, Tian
  • Deng, Yong

Abstract

Predicting missing links is of both theoretical value and practical interest in network science. In this paper, we empirically investigate a new link prediction method base on similarity and compare nine well-known local similarity measures on nine real networks. Most of the previous studies focus on the accuracy, however, it is crucial to consider the link predictability as an initial property of networks itself. Hence, this paper has proposed a new link prediction approach called evidential measure (EM) based on Dempster–Shafer theory. Moreover, this paper proposed a new method to measure link predictability via local information and Shannon entropy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:482:y:2017:i:c:p:699-712
    DOI: 10.1016/j.physa.2017.04.106
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    4. 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.

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