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Integrating Latent Feature Model and Kernel Function for Link Prediction in Bipartite Networks

  • Xue Chen
  • Wenjun Wang
  • Yueheng SunEmail author
  • Bin Hu
  • Pengfei Jiao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

Link prediction aims to infer missing links or predict future links from existing network structure. In recent years, most studies of link prediction mainly focus on monopartite networks. However, a class of complex systems can be represented by bipartite networks, which containing two different types of nodes and the no links exist in the same type. In this paper, we propose Kernel-based Latent Feature Models (KLFM) framework which can extract nonlinear high-order information in the existing network through kernel-based mappings. Then a kernel-based iterative rule has been developed. Extensive experiments on eight disparate real-world bipartite networks demonstrate that the KLFM framework achieves a more robust and explicable performance than other methods.

Keywords

Link prediction Bipartite network Latent feature model Kernel function 

Notes

Acknowledgment

The National Key R&D Program of China (2018YFC0809800, 2016QY15Z2502-02, 2018YFC0831000).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Xue Chen
    • 1
  • Wenjun Wang
    • 1
  • Yueheng Sun
    • 1
    Email author
  • Bin Hu
    • 2
  • Pengfei Jiao
    • 3
  1. 1.School of College of Intelligence and ComputingTianjin UniversityTianjinChina
  2. 2.School of Technical College for the DeafTianjin University of TechnologyTianjinChina
  3. 3.School of Center of Biosafety Research and StrategyTianjin UniversityTianjinChina

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