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Mining relationships between performance of link prediction algorithms and network structure

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

Abstract

The numerous link prediction algorithms proposed by the network science researchers demonstrate their creativity in this hot topic. However, various algorithms together with the miscellaneous real-world networks put much difficulty on the choice of algorithm when coping with a new network. In this paper, we try to provide some elementary rules through mining the relationships between network structure features and the algorithm mechanisms. We discovered some principles indicating clustering coefficients influences on the prediction accuracy of structure-based algorithms. Besides, our experiment results present some interesting phenomenon neglected previously. The results and discussions may help us understand the link prediction problem better and further.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:153:y:2021:i:p2:s0960077921008390
    DOI: 10.1016/j.chaos.2021.111485
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    References listed on IDEAS

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    1. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
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    3. 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.
    4. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
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