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Link Prediction in Complex Network via Penalizing Noncontribution Relations of Endpoints

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  • Xuzhen Zhu
  • Yang Tian
  • Hui Tian

Abstract

Similarity based link prediction algorithms become the focus in complex network research. Although endpoint degree as source of influence diffusion plays an important role in link prediction, some noncontribution links, also called noncontribution relations, involved in the endpoint degree serve nothing to the similarity between the two nonadjacent endpoints. In this paper, we propose a novel link prediction algorithm to penalize those endpoints’ degrees including many null links in influence diffusion, namely, noncontribution relations penalization algorithm, briefly called NRP. Seven mainstream baselines are introduced for comparison on nine benchmark datasets, and numerical analysis shows great improvement of accuracy performance, measured by the Area Under roc Curve (AUC). At last, we simply discuss the complexity of our algorithm.

Suggested Citation

  • Xuzhen Zhu & Yang Tian & Hui Tian, 2014. "Link Prediction in Complex Network via Penalizing Noncontribution Relations of Endpoints," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, November.
  • Handle: RePEc:hin:jnlmpe:453546
    DOI: 10.1155/2014/453546
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    Cited by:

    1. Namika Makhija & Shashank Mouli Satapathy, 2021. "Community detection in dynamic networks: a comprehensive and comparative review using external and internal criteria," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(2), pages 217-230, April.

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