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Link prediction via significant influence

Author

Listed:
  • Yang, Yujie
  • Zhang, Jianhua
  • Zhu, Xuzhen
  • Tian, Lei

Abstract

In traditional link prediction, many researches assume that endpoint influence, represented by endpoint degree, prefers to facilitate the connection between big-degree endpoints. However, after investigating the network structure, it is observed that influence is determined by the relations built through the paths between endpoints instead of the endpoint degree. Strong relations connecting the other endpoint through short paths, especially through common neighbors, can bring in more powerful influence, and in contrast, those relations through long paths obviously generate weak influence. In this paper, a novel link prediction index SI is proposed, which deliberately models the significant influence by distinguishing the strong influence from the weak. After comparison with main stream baselines on 12 benchmark datasets, the results suggest SI effectively improve the link prediction accuracy.

Suggested Citation

  • Yang, Yujie & Zhang, Jianhua & Zhu, Xuzhen & Tian, Lei, 2018. "Link prediction via significant influence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1523-1530.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:1523-1530
    DOI: 10.1016/j.physa.2017.11.078
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    Citations

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    Cited by:

    1. Aghabozorgi, Farshad & Khayyambashi, Mohammad Reza, 2018. "A new similarity measure for link prediction based on local structures in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 12-23.
    2. 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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