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)


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.


Link prediction Bipartite network Latent feature model Kernel function 



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


  1. 1.
    Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A 390(6), 1150–1170 (2011)CrossRefGoogle Scholar
  2. 2.
    Yildirim, M.A., Coscia, M.: Using random walks to generate associations between objects. PLoS ONE 9(8), e104813 (2014)CrossRefGoogle Scholar
  3. 3.
    Gao, M., Chen, L., Li, B., Li, Y., Liu, W., Xu, Y.C.: Projection-based link prediction in a bipartite network. Inf. Sci. 376, 158–171 (2017)CrossRefGoogle Scholar
  4. 4.
    Zhou, T., Ren, J., Medo, M., Zhang, Y.C.: Bipartite network projection and personal recommendation. Phys. Rev. E 76(4), 046115 (2007)CrossRefGoogle Scholar
  5. 5.
    Daminelli, S., Thomas, J.M., Durán, C., Cannistraci, C.V.: Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks. New J. Phys. 17(11), 113037 (2015)CrossRefGoogle Scholar
  6. 6.
    Newman, M.E.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)CrossRefGoogle Scholar
  7. 7.
    Durán, C., Daminelli, S., Thomas, J.M., Haupt, V.J., Schroeder, M., Cannistraci, C.V.: Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory. Brief. Bioinform. 19(6), 1183–1202 (2017)CrossRefGoogle Scholar
  8. 8.
    Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2001)Google Scholar
  9. 9.
    Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 437–452. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Wang, W., Chen, X., Jiao, P., Jin, D.: Similarity-based regularized latent feature model for link prediction in bipartite networks. Sci. Rep. 7(1), 16996 (2017)CrossRefGoogle Scholar
  11. 11.
    Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., Kanehisa, M.: Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13), 232–240 (2008)CrossRefGoogle Scholar
  12. 12.
    Davis, A., Gardner, B.B., Gardner, M.R.: Deep South: A Social Anthropological Study of Caste and Class. University of South Carolina Press, Columbia (2009)Google Scholar
  13. 13.
    Larremore, D.B., Clauset, A., Buckee, C.O.: A network approach to analyzing highly recombinant malaria parasite genes. PLoS Comput. Biol. 9(10), e1003268 (2013)CrossRefGoogle Scholar
  14. 14.
    Yamanishi, Y., Kotera, M., Moriya, Y., Sawada, R., Kanehisa, M., Goto, S.: DINIES: drug-target interaction network inference engine based on supervised analysis. Nucleic Acids Res. 42(W1), 39–45 (2014)CrossRefGoogle Scholar
  15. 15.
    Coscia, M., Hausmann, R., Hidalgo, C.A.: The structure and dynamics of international development assistance. J. Globalization Dev. 3(2), 1–42 (2013)CrossRefGoogle Scholar
  16. 16.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  17. 17.
    Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982)CrossRefGoogle Scholar

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

Personalised recommendations