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Connecting Patterns Inspire Link Prediction in Complex Networks

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  • Ming-Yang Zhou
  • Hao Liao
  • Wen-Man Xiong
  • Xiang-Yang Wu
  • Zong-Wen Wei

Abstract

Link prediction uses observed data to predict future or potential relations in complex networks. An underlying hypothesis is that two nodes have a high likelihood of connecting together if they share many common characteristics. The key issue is to develop different similarity-evaluating approaches. However, in this paper, by characterizing the differences of the similarity scores of existing and nonexisting links, we find an interesting phenomenon that two nodes with some particular low similarity scores also have a high probability to connect together. Thus, we put forward a new framework that utilizes an optimal one-variable function to adjust the similarity scores of two nodes. Theoretical analysis suggests that more links of low similarity scores (long-range links) could be predicted correctly by our method without losing accuracy. Experiments in real networks reveal that our framework not only enhances the precision significantly but also predicts more long-range links than state-of-the-art methods, which deepens our understanding of the structure of complex networks.

Suggested Citation

  • Ming-Yang Zhou & Hao Liao & Wen-Man Xiong & Xiang-Yang Wu & Zong-Wen Wei, 2017. "Connecting Patterns Inspire Link Prediction in Complex Networks," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    • Handle: RePEc:hin:complx:8581365
    DOI: 10.1155/2017/8581365
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    Cited by:

    1. Ren, Baoan & Zhang, Yu & Chen, Jing & Shen, Lincheng, 2019. "Efficient network disruption under imperfect information: The sharpening effect of network reconstruction with no prior knowledge," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 196-207.

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