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Two-Phase Approach to Link Prediction

  • Srinivas Virinchi
  • Pabitra Mitra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8835)

Abstract

Link prediction deals with predicting edges which are likely to occur in the future. The clustering coefficient of sparse networks is typically small. Link prediction performs poorly on networks having low clustering coefficient and it improves with increase in clustering coefficient. Motivated by this, we propose an approach, wherein, we add relevant non-existent edges to the sparse network to form an auxiliary network. In contrast to the classical link prediction algorithm, we use the auxiliary network for link prediction. This auxiliary network has higher clustering coefficient compared to the original network. We formally justify our approach in terms of Kullback-Leibler (KL) Divergence and Clustering Coefficient of the social network. Experiments on several benchmark datasets show an improvement of upto 15% by our approach compared to the standard approach.

Keywords

Graph Mining Local Similarity KL Divergence Clustering Coefficient Power-law degree distribution 

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References

  1. 1.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. In: Proc. of CIKM (2003)Google Scholar
  2. 2.
    Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A 390(6), 1150–1170 (2011)CrossRefGoogle Scholar
  3. 3.
    Al Hasan, M., Zaki, M.J.: A survey of link prediction in social networks. In: Social Network Data Analytics, pp. 243–275. Springer (2011)Google Scholar
  4. 4.
    Adamic, L.A., Adar, E.: Friends and neighbors on the web. Social Networks 25(3), 211–230 (2003)CrossRefGoogle Scholar
  5. 5.
    Newman, M.E.J.: Clustering and preferential attachment in growing networks. Physical Review E 64(2), 025102 (2001)CrossRefGoogle Scholar
  6. 6.
    Zhou, T., Lü, L., Zhang, Y.: Predicting missing links via local information. The European Physical Journal B 71(4), 623–630 (2009)CrossRefzbMATHGoogle Scholar
  7. 7.
    Soundarajan, S., Hopcroft, J.: Using community information to improve the precision of link prediction methods. In: Proc. of WWW (2012)Google Scholar
  8. 8.
    Virinchi, S., Mitra, P.: Similarity measures for link prediction using power law degree distribution. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part II. LNCS, vol. 8227, pp. 257–264. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Newman, M.E.J.: Networks: an introduction. Oxford University Press (2009)Google Scholar
  10. 10.
    Liu, Z., He, J., Srivastava, J.: Cliques in complex networks reveal link formation and community evolution. arXiv preprint arXiv:1301.0803 (2013)Google Scholar
  11. 11.
    Bloznelis, M.: Degree and clustering coefficient in sparse random intersection graphs. The Annals of Applied Probability 23(3), 1254–1289 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: Densification and shrinking diameters. ACM TKDD 1(1), 2 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Srinivas Virinchi
    • 1
  • Pabitra Mitra
    • 1
  1. 1.Dept of Computer Science and EngineeringIndian Institute of Technology KharagpurIndia

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