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This study introduces SASNMF, a novel link prediction framework. It effectively integrates network structure with auxiliary information, outperforming existing methods in predicting missing links.

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Area of Science:

  • Network Science
  • Data Mining
  • Machine Learning

Background:

  • Link prediction aims to infer missing connections in incomplete networks.
  • Current methods often neglect external information, limiting their accuracy.
  • Social influence and homophily highlight the importance of both structure and attributes.

Purpose of the Study:

  • To propose SASNMF, a unified framework for link prediction.
  • To incorporate graph structure, node attributes, and latent features.
  • To evaluate the framework's performance using diverse real-world networks.

Main Methods:

  • Developed SASNMF, a non-negative matrix factorization-based framework.
  • Integrated internal (node attributes) and external (latent features) information.
  • Tested three distinct combinations of information inputs.

Main Results:

  • SASNMF demonstrated competitive performance against benchmark and state-of-the-art methods.
  • Experiments were conducted on thirteen real-world networks (five attribute, eight non-attribute).
  • The framework showed superiority in link prediction tasks.

Conclusions:

  • SASNMF effectively leverages auxiliary information for improved link prediction.
  • The unified framework offers a robust approach to handling incomplete network data.
  • Integrating diverse information sources enhances the accuracy of missing link inference.