Advertisement

TeleLink: Link Prediction in Social Network Based on Multiplex Cohesive Structures

Conference paper
  • 890 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9708)

Abstract

Given a network where the same set of nodes have multiple types of relationships, how do we efficiently predict potential links in the future (e.g., interactions between social actors), and how do we predict links using information from other relationships? These problems have been widely studied recently, most of the existing methods either aggregate multiple types of relationships into a single network or consider them separately and ignore the correlations across relationships, leading to information loss. In this work, we present TeleLink, a general link prediction model that works for networks with single and multiple relationships. TeleLink predicts potential links based on community detection and improves link prediction by bringing in a cohesive structure across multiple networks constructed by different relationships or node attributes. To further improve the prediction performance, we extend TeleLink to a semi-supervised scheme, incorporating partially labeled information. Our extensive experiments show that TeleLink outperforms existing methods in predicting new links. Specifically, among the various datasets that we study, TeleLink achieves a precision improvement by up to 110 % compared to the baselines.

References

  1. 1.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. JASIST 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  2. 2.
    Hasan, M.A., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: SDM 2006: Workshop (2006)Google Scholar
  3. 3.
    Stumpf, M.P.H., Thomas, T., et al.: Estimating the size of the human interactome. PNAS 105(19), 6959–6964 (2008)CrossRefGoogle Scholar
  4. 4.
    Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications 390(6), 1150–1170 (2011)CrossRefGoogle Scholar
  5. 5.
    Jaccard, P.: Distribution de la Flore Alpine: DANS le Bassin des dranses et dans quelques régions voisines. Rouge (1901)Google Scholar
  6. 6.
    Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)CrossRefzbMATHGoogle Scholar
  7. 7.
    Feng, X., Zhao, J.C., Xu, K.: Link prediction in complex networks: a clustering perspective. Eur. Phys. J. B 85(1), 1–9 (2012)CrossRefGoogle Scholar
  8. 8.
    Clauset, A., Moore, C., Newman, M.E.J.: Hierarchical structure and the prediction of missing links in networks. Nature 453(7191), 98–101 (2008)CrossRefGoogle Scholar
  9. 9.
    Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 243–252. ACM (2010)Google Scholar
  10. 10.
    De Domenico, M., Lancichinetti, A., Arenas, A., Rosvall, M.: Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys. Rev. X 5(1), 011027 (2015)Google Scholar
  11. 11.
    Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)CrossRefGoogle Scholar
  12. 12.
    Brin, S., Page, L.: Reprint of: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)CrossRefGoogle Scholar
  13. 13.
    Backstrom, L., et al.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM WSDM, pp. 635–644. ACM (2011)Google Scholar
  14. 14.
    Soundarajan, S., Hopcroft, J.: Using community info. to improve the precision of link prediction methods. In: Proceedings of the WWW, pp. 607–608. ACM (2012)Google Scholar
  15. 15.
    Davis, D., Lichtenwalter, R., Chawla, N.V.: Supervised methods for multi-relational link prediction. Soc. Netw. Anal. Min. 3(2), 127–141 (2013)CrossRefGoogle Scholar
  16. 16.
    Sun, Y., et al.: Co-author relationship prediction in heterogeneous bibliographic networks. In: 2011 International Conference on ASONAM, pp. 121–128. IEEE (2011)Google Scholar
  17. 17.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. PNAS 105(4), 1118–1123 (2008)CrossRefGoogle Scholar
  18. 18.
    Stehlé, J., et al.: High-resolution measurements of face-to-face contact patterns in a primary school (2011)Google Scholar
  19. 19.
    Gemmetto, V., Barrat, A., Cattuto, C.: Mitigation of infectious disease at school: targeted class closure vs school closure. BMC Infect. Dis. 14(1), 695 (2014)CrossRefGoogle Scholar
  20. 20.
    Lin, Y.-R., Keegan, B., Margolin, D., Lazer, D.: Rising tides or rising stars?: Dynamics of shared attention on twitter during media events (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA
  2. 2.Intelligent System ProgramUniversity of PittsburghPittsburghUSA
  3. 3.School of Information SciencesUniversity of PittsburghPittsburghUSA

Personalised recommendations