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Prediction of missing links based on multi-resolution community division

Author

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  • Ding, Jingyi
  • Jiao, Licheng
  • Wu, Jianshe
  • Hou, Yunting
  • Qi, Yutao

Abstract

The investigation of link prediction in networks is an important issue in many disciplines. The research of prediction algorithms which required short time but high accuracy is still a challenging task. Most of the existing algorithms are based on the topological information of the networks, including the local or global similarity indices. It is found that the hierarchical organization and community structure information may indeed provide insights for link prediction. In this paper, we propose a simple link prediction method, which fully explore the community structure information of the networks. Firstly, the community structure of the networks under different resolutions is extracted. Then, a simple frequency statistical model is applied to calculate how many times that a pair of nodes divided into the same community under different resolutions. Finally, the probability of the missing links is calculated. The performance of our algorithm is demonstrated by comparing with other seven well-known methods on two kinds of networks in different scales. The results indicate that our approach not only has a good performance on the accuracy, but also has a lower time complexity than any other algorithms which are based on hierarchical structure of the network.

Suggested Citation

  • Ding, Jingyi & Jiao, Licheng & Wu, Jianshe & Hou, Yunting & Qi, Yutao, 2015. "Prediction of missing links based on multi-resolution community division," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 76-85.
    • Handle: RePEc:eee:phsmap:v:417:y:2015:i:c:p:76-85
    DOI: 10.1016/j.physa.2014.09.005
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    References listed on IDEAS

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    Cited by:

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    5. 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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