Link prediction in complex networks: a clustering perspective

Regular Article


Link prediction is an open problem in the complex network, which attracts much research interest currently. However, little attention has been paid to the relation between network structure and the performance of prediction methods. In order to fill this vital gap, we try to understand how the network structure affects the performance of link prediction methods in the view of clustering. Our experiments on both synthetic and real-world networks show that as the clustering grows, the accuracy of these methods could be improved remarkably, while for the sparse and weakly clustered network, they perform poorly. We explain this through the distinguishment caused by increased clustering between the score distribution of positive and negative instances. Our finding also sheds light on the problem of how to select appropriate approaches for different networks with various densities and clusterings.


Statistical and Nonlinear Physics 


  1. 1.
    L. Getoor, C.P. Diehl, SIGKDD Explor. Newsl. 7, 3 (2005)CrossRefGoogle Scholar
  2. 2.
    L. Lü, T. Zhou, Physica A 390, 1150 (2011)CrossRefADSGoogle Scholar
  3. 3.
    M. Bilgic, G.M. Namata, L. Getoor, Combining Collective Classification and Link Prediction, in Proceedings of the Seventh IEEE International Conference on Data Mining Workshops, ICDMW ’07 (IEEE Computer Society, Washington, DC, USA, 2007), pp. 381–386Google Scholar
  4. 4.
    M.J. Rattigan, D. Jensen, SIGKDD Explor. Newsl. 7, 41 (2005)CrossRefGoogle Scholar
  5. 5.
    D. Liben-Nowell, J. Kleinberg, The link prediction problem for social networks, in Proceedings of the twelfth international conference on Information and knowledge management, CIKM ’03 (ACM, New York, NY, USA, 2003), pp. 556–559Google Scholar
  6. 6.
    T. Zhou, L. Lü, Y.C. Zhang, Eur. Phys. J. B 71, 623 (2009)CrossRefMATHADSGoogle Scholar
  7. 7.
    W. Liu, L. Lü, Europhys. Lett. 89 (2010)Google Scholar
  8. 8.
    A. Clauset, C. Moore, M.E.J. Newman, Nature 453, 98 (2008)CrossRefADSGoogle Scholar
  9. 9.
    R. Guimerà, M. Sales-Pardo, Proc. Natl. Acad. Sci. USA 106, 22073 (2009)CrossRefADSGoogle Scholar
  10. 10.
    M.A. Hasan, V. Chaoji, S. Salem, M. Zaki, Link Prediction Using Supervised Learning, in Proc. of SDM 06 workshop on Link Analysis, Counterterrorism and Security (2006)Google Scholar
  11. 11.
    J. O’Madadhain, J. Hutchins, P. Smyth, SIGKDD Explor. Newsl. 7, 23 (2005)CrossRefGoogle Scholar
  12. 12.
    C.W.K. Leung, E.P. Lim, D. Lo, J. Weng, Mining interesting link formation rules in social networks, in Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM ’10 (ACM, New York, NY, USA, 2010), pp. 209–218Google Scholar
  13. 13.
    D. Romero, J. Kleinberg, The Directed Closure Process in Hybrid Social-Information Networks, with an Analysis of Link Formation on Twitter, in International AAAI Conference on Weblogs and Social Media (2010)Google Scholar
  14. 14.
    T. Murata, S. Moriyasu, Link Prediction of Social Networks Based on Weighted Proximity Measures, in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI ’07 (IEEE Computer Society, Washington, DC, USA, 2007), pp. 85–88Google Scholar
  15. 15.
    L.A. Adamic, E. Adar, Social Netw. 25, 211 (2001)CrossRefGoogle Scholar
  16. 16.
    H.H. Song, T.W. Cho, V. Dave, Y. Zhang, L. Qiu, Scalable proximity estimation and link prediction in online social networks, in Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference, IMC ’09 (ACM, New York, NY, USA, 2009), pp. 322–335Google Scholar
  17. 17.
    A.L. Barabasi, R. Albert, Science 286, 509 (1999)CrossRefMathSciNetGoogle Scholar
  18. 18.
    P. Erdös, A. Rényi, Publ. Math. Inst. Hung. Acad. Sci. 5, 17 (1960)MATHGoogle Scholar
  19. 19.
    M.E.J. Newman, Phys. Rev. E 74, 036104 (2006)CrossRefADSMathSciNetGoogle Scholar
  20. 20.
    D.J. Watts, S.H. Strogatz, Nature 393, 440 (1998)CrossRefADSGoogle Scholar
  21. 21.
    R. Ackland, Presentation to Blog Talk Downunder (2005)Google Scholar
  22. 22.
    B.J. Kim, Phys. Rev. E 69, 045101 (2004)CrossRefADSGoogle Scholar
  23. 23.
    X. Ma, L. Huang, Y.C. Lai, Z. Zheng, Phys. Rev. E 79, 056106 (2009)CrossRefADSGoogle Scholar

Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.State Key Laboratory of Software Development EnvironmentBeihang UniversityBeijingP.R. China

Personalised recommendations