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Extended resource allocation index for link prediction of complex network

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  • Liu, Shuxin
  • Ji, Xinsheng
  • Liu, Caixia
  • Bai, Yi

Abstract

Recently, a number of similarity-based methods have been proposed to predict the missing links in complex network. Among these indices, the resource allocation index performs very well with lower time complexity. However, it ignores potential resources transferred by local paths between two endpoints. Motivated by the resource exchange taking places between endpoints, an extended resource allocation index is proposed. Empirical study on twelve real networks and three synthetic dynamic networks has shown that the index we proposed can achieve a good performance, compared with eight mainstream baselines.

Suggested Citation

  • Liu, Shuxin & Ji, Xinsheng & Liu, Caixia & Bai, Yi, 2017. "Extended resource allocation index for link prediction of complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 174-183.
    • Handle: RePEc:eee:phsmap:v:479:y:2017:i:c:p:174-183
    DOI: 10.1016/j.physa.2017.02.078
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    References listed on IDEAS

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    Cited by:

    1. Akrati Saxena & George Fletcher & Mykola Pechenizkiy, 2022. "HM-EIICT: Fairness-aware link prediction in complex networks using community information," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2853-2870, November.
    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.
    3. Chi, Kuo & Qu, Hui & Yin, Guisheng, 2022. "Link prediction for existing links in dynamic networks based on the attraction force," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

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