Advertisement

Link Prediction Based on Weighted Networks

  • Zeyao Yang
  • Damou Fu
  • Yutian Tang
  • Yongbo Zhang
  • Yunsheng Hao
  • Chen Gui
  • Xu Ji
  • Xin Yue
Part of the Communications in Computer and Information Science book series (CCIS, volume 324)

Abstract

Link Prediction can make networks more complete. However, because of restraint of algorithm, traditional link-prediction measures cannot make full use of weight information to analyze the network. To solve this problem, this paper proposes a new method based on weighted networks, and the new method synthesizes and improves existent methods so that the predictor could make use of weight information in the network. We apply the new method to three real networks (astro-ph, cond-mat and hep-th). The result of experiment demonstrates that new method is more precise, and this method provides people with a new idea about how to better analyze weighted networks.

Keywords

complex network link prediction weighted network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  2. 2.
    Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Proceedings of the ACM SIGKDD 2006, pp. 611–617. ACM Press, New York (2006)Google Scholar
  3. 3.
    Sarukkai, R.R.: Link prediction and path analysis using markov chains. Computer Networks 33(1-6), 377–386 (2000)CrossRefGoogle Scholar
  4. 4.
    Zhu, J., Hong, J., Hughes, J.G.: Using Markov Chains for Link Prediction in Adaptive Web Sites. In: Bustard, D.W., Liu, W., Sterritt, R. (eds.) Soft-Ware 2002. LNCS, vol. 2311, pp. 60–73. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Popescul, A., Ungar, L.: Statistical relational learning for link prediction. In: Proceedings of the Workshop on Learning Statistical Models from Relational Data, pp. 81–87. ACM Press, New York (2003)Google Scholar
  6. 6.
    O’Madadhain, J., Hutchins, J., Smyth, P.: Prediction and ranking algorithms for event-based network data. In: Proceedings of the ACM SIGKDD 2005, pp. 23–30. ACM Press, New York (2005)Google Scholar
  7. 7.
    Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th Intl. Conf. Mach. Learn., pp. 296–304. Morgan Kaufman Publishers, San Francisco (1998)Google Scholar
  8. 8.
    Clauset, A., Moore, C., Newman, M.E.J.: Hierarchical structure and the prediction of missing links in networks. Nature 453, 98–101 (2008)CrossRefGoogle Scholar
  9. 9.
    Holland, P.W., Laskey, K.B., Leinhard, S.: Stochastic blockmodels: First steps. Social Networks 5, 109–137 (1983)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Getoor, L., Diehl, C.P.: Link mining: a survey. ACM SIGKDD Explorations Newsletter 7(2), 3–12 (2005)CrossRefGoogle Scholar
  11. 11.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. & ISDN Syst. 30(1-7), 107–117 (1998)CrossRefGoogle Scholar
  12. 12.
    Tong, H., Faloutsos, C., Pan, J.Y.: Fast random walk with restart and its applications. In: Proceedings of the 6th Intl. Conf. Data Min., pp. 613–622. IEEE Press, Washington, DC (2006)Google Scholar
  13. 13.
    Shang, M.S., Lü, L., Zeng, W., et al.: Relevance is more significant than correlation: Information filtering on sparse data. Europhys. Lett. 88(6), 68008 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zeyao Yang
    • 1
  • Damou Fu
    • 1
  • Yutian Tang
    • 1
  • Yongbo Zhang
    • 1
  • Yunsheng Hao
    • 1
  • Chen Gui
    • 1
  • Xu Ji
    • 1
  • Xin Yue
    • 1
  1. 1.College of Computer Science and TechnologyJilin UniversityChina

Personalised recommendations