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Gravity Effects on Information Filtering and Network Evolving

Author

Listed:
  • Jin-Hu Liu
  • Zi-Ke Zhang
  • Lingjiao Chen
  • Chuang Liu
  • Chengcheng Yang
  • Xueqi Wang

Abstract

In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model.

Suggested Citation

  • Jin-Hu Liu & Zi-Ke Zhang & Lingjiao Chen & Chuang Liu & Chengcheng Yang & Xueqi Wang, 2014. "Gravity Effects on Information Filtering and Network Evolving," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-7, March.
    • Handle: RePEc:plo:pone00:0091070
    DOI: 10.1371/journal.pone.0091070
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Yin & Gao, Kening & Zhang, Bin, 2015. "The concept exploration model and an application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 430-442.

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