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Link prediction based on non-negative matrix factorization

Author

Listed:
  • Bolun Chen
  • Fenfen Li
  • Senbo Chen
  • Ronglin Hu
  • Ling Chen

Abstract

With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like information science, anthropology, sociology and computer sciences. It makes requirements for effective link prediction techniques to extract the most essential and relevant information for online users in internet. Therefore, this paper attempts to put forward a link prediction algorithm based on non-negative matrix factorization. In the algorithm, we reconstruct the correlation between different types of matrix through the projection of high-dimensional vector space to a low-dimensional one, and then use the similarity between the column vectors of the weight matrix as the scoring matrix. The experiment results demonstrate that the algorithm not only reduces data storage space but also effectively makes the improvements of the prediction performance during the process of sustaining a low time complexity.

Suggested Citation

  • Bolun Chen & Fenfen Li & Senbo Chen & Ronglin Hu & Ling Chen, 2017. "Link prediction based on non-negative matrix factorization," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-18, August.
  • Handle: RePEc:plo:pone00:0182968
    DOI: 10.1371/journal.pone.0182968
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    Cited by:

    1. Peng Liu & Liang Gui & Huirong Wang & Muhammad Riaz, 2022. "A Two-Stage Deep-Learning Model for Link Prediction Based on Network Structure and Node Attributes," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    2. Kumar, Ajay & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction techniques, applications, and performance: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).

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