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Predicting link directions using local directed path

Author

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  • Wang, Xiaojie
  • Zhang, Xue
  • Zhao, Chengli
  • Xie, Zheng
  • Zhang, Shengjun
  • Yi, Dongyun

Abstract

Link prediction in directed network is attracting growing interest among many network scientists. Compared with predicting the existence of a link, determining its direction is more complicated. In this paper, we propose an efficient solution named Local Directed Path to predict link direction. By adding an extra ground node to the network, we solve the information loss problem in sparse network, which makes our method effective and robust. As a quasi-local method, our method can deal with large-scale networks in a reasonable time. Empirical analysis on real networks shows that our method can correctly predict link directions, which outperforms some local and global methods.

Suggested Citation

  • Wang, Xiaojie & Zhang, Xue & Zhao, Chengli & Xie, Zheng & Zhang, Shengjun & Yi, Dongyun, 2015. "Predicting link directions using local directed path," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 260-267.
    • Handle: RePEc:eee:phsmap:v:419:y:2015:i:c:p:260-267
    DOI: 10.1016/j.physa.2014.10.007
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    References listed on IDEAS

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    Cited by:

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    2. Orzechowski, Kamil P. & Mrowinski, Maciej J. & Fronczak, Agata & Fronczak, Piotr, 2023. "Asymmetry of social interactions and its role in link predictability: The case of coauthorship networks," Journal of Informetrics, Elsevier, vol. 17(2).

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