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Cold-start link prediction in multi-relational networks based on network dependence analysis

Author

Listed:
  • Wu, Shun-yao
  • Zhang, Qi
  • Xue, Chuan-yu
  • Liao, Xi-yang

Abstract

Cold-start link prediction has been a hot issue in complex network. Different with most of existing methods, this paper utilizes multiple interactions to predict a specific type of links. In this paper, multiple interactions are abstracted as multi-relational networks, and robust principle component analysis is employed to extract low-dimensional latent factors from sub-networks. Then a distribution free independence test, projection correlation, is introduced to efficiently analyze dependence between target and auxiliary sub-networks. Furthermore, associated auxiliary networks are exploited for cold-start link prediction, which aims to forecast potential links for new/isolated nodes in target sub-networks. Experimental results on 8 bioinformatics datasets validate rationality and effectiveness of the method.

Suggested Citation

  • Wu, Shun-yao & Zhang, Qi & Xue, Chuan-yu & Liao, Xi-yang, 2019. "Cold-start link prediction in multi-relational networks based on network dependence analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 558-565.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:558-565
    DOI: 10.1016/j.physa.2018.09.082
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    Citations

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    Cited by:

    1. Tang, Minghu & Wang, Wenjun, 2022. "Cold-start link prediction integrating community information via multi-nonnegative matrix factorization," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    2. Yuliansyah, Herman & Othman, Zulaiha Ali & Bakar, Azuraliza Abu, 2023. "A new link prediction method to alleviate the cold-start problem based on extending common neighbor and degree centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).

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