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Link prediction on bipartite networks using matrix factorization with negative sample selection.

Siqi Peng1, Akihiro Yamamoto1, Kimihito Ito2

  • 1Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan.

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This study introduces a new bipartite link prediction method using matrix factorization with negative sample selection. Our approach improves accuracy by selecting reliable negative training samples, outperforming existing unsupervised methods.

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Area of Science:

  • Network Science
  • Data Mining
  • Machine Learning

Background:

  • Bipartite link prediction aims to identify missing relationships in bipartite networks.
  • Matrix factorization (MF) is a common technique but requires accurate training data for both present and absent links.
  • The absence of ground truth for missing links poses a challenge for MF-based prediction.

Purpose of the Study:

  • To develop an improved method for bipartite link prediction.
  • To address the challenge of unavailable ground truth for absent links in bipartite networks.
  • To enhance the performance of matrix factorization for link prediction.

Main Methods:

  • A novel negative sample selection technique is proposed.
  • Formal Concept Analysis (FCA) is utilized to identify reliable negative training samples.
  • The selected negative samples are integrated into a matrix factorization (MF) process.

Main Results:

  • The proposed joint method significantly outperforms raw MF-based link prediction.
  • Experimental results demonstrate superior performance compared to existing unsupervised link prediction methods.
  • The technique proves effective in hypothetical application scenarios.

Conclusions:

  • Negative sample selection using FCA enhances bipartite link prediction accuracy.
  • The proposed method offers a robust solution for networks with incomplete negative link information.
  • This approach advances the field of unsupervised link prediction in bipartite networks.