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An intermediary probability model for link prediction

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  • Zhang, Xuejun
  • Pang, Wenbo
  • Xia, Yongxiang

Abstract

Among the numerous link prediction algorithms in complex networks, similarity-based algorithms play an important role due to promising accuracy and low computational complexity. Apart from the classical CN-based indexes, several interdisciplinary methods provide new ideas to this problem and achieve improvements in some aspects. In this article, we propose a new model from the perspective of an intermediary process and introduce indexes under the framework, which show better performance for precision. Combined with k-shell decomposition, our deeper analysis gives a reasonable explanation and presents an insight on classical and proposed algorithms, which can further contribute to the understanding of link prediction problem.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:512:y:2018:i:c:p:902-912
    DOI: 10.1016/j.physa.2018.08.068
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    References listed on IDEAS

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    Cited by:

    1. Jiang, Zhongyuan & Tang, Xiaoke & Zeng, Yong & Li, Jinku & Ma, Jianfeng, 2021. "Adversarial link deception against the link prediction in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 577(C).
    2. Xia, Yongxiang & Pang, Wenbo & Zhang, Xuejun, 2021. "Mining relationships between performance of link prediction algorithms and network structure," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).

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