Advertisement

Community-Based Link Prediction in Social Networks

  • Rong KuangEmail author
  • Qun Liu
  • Hong Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9713)

Abstract

Link prediction has attracted wide attention in the related fields of social networks which has been widely used in many domains, such as, identifying spurious interactions, extracting missing information, evaluating evolving mechanism of complex networks. But all of the previous works do not considering the influence of the neighbors and just applying in small networks. In this paper, a new similarity algorithm is proposed, which is motivated by the herd phenomenon taking place on network. Moreover, it is found that many links are assigned low scores while it has a longer path. Therefore, if such links the longer path has not been taken into account, which can improve the efficiency of time further, especially in large-scale networks. Extensive experiments were conducted on five real-world social networks, compared with the representative node similarity-based methods, our proposed model can provide more accurate predictions.

Keywords

Link prediction Community structure Common neighbor Herd phenomenon 

Notes

Acknowledgments

This work is partly funded by the National Nature Science Foundation of China (61075019) and the Natural Science Foundation of Chongqing (CSTC2014jcyjA40047) and the Municipal Education Commission research project of Chongqing (KJ1400403).

References

  1. 1.
    Lü, L.Y., Zhou, T.: Link prediction in complex networks: a survey. Phys. A: Stat. Mech. Appl. 390, 1150–1170 (2011)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    O’Madadhain, J., Hutchins, J., Smyth, P.: Prediction and ranking algorithms for event-based network data. ACM SIGKDD 7(2), 23–30 (2005)CrossRefGoogle Scholar
  4. 4.
    Han, X., Wang, L.Y., Chen C., Farahbakhsh, R.: Link prediction for new users in social networks. In: ICC, pp. 1250–1255 (2015)Google Scholar
  5. 5.
    Lü, L.Y., Pan, L.M., Zhou, T., Zhang, Y.C., Stanley, H.E.: Toward link predictability of complex networks. PANS 112, 2325–2330 (2015)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Zhang, W.Y., Wu, B.: Accurate and fast link prediction in complex networks. In: Natural Computation (ICNC), pp. 653–657 (2014)Google Scholar
  7. 7.
    Yin, G., Yin W.S., Dong, Y.X.: A new link prediction algorithm: node link strength algorithm. In: SCAC, pp. 5–9 (2014)Google Scholar
  8. 8.
    Abir, D., Nilloy, G., Soumen, C.: Discriminative link prediction using local links, node features and community structure. In: Data Mining (ICDM), pp. 1009–1018 (2013)Google Scholar
  9. 9.
    Valverde-Rebaza, J., Lopes, A.: Exploiting behaviors of communities of twitter users for link prediction. Soc. Netw. Anal. Min. 3, 1063–1074 (2013)CrossRefGoogle Scholar
  10. 10.
    Li, X., Chen, H.: Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis. Support Syst. 54, 880–890 (2013)CrossRefGoogle Scholar
  11. 11.
    Scellato, S., Noulas, A., Mascolo, C.: Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, pp. 1046–1054 (2011)Google Scholar
  12. 12.
    Chen, Z., Zhang, W.: A marginalized denoising method for link prediction in relational data. In: Proceedings of the 2014 SIAM International Conference on Data Mining, Philadelphia, pp. 298–306 (2014)Google Scholar
  13. 13.
    Menon, K., Elkan, C.: Link prediction via matrix factorization. In: Proceedings of the 2011 European Conference on Machine Learning and Knowledge Discovery in Databases, Athens, pp. 437–452 (2011)Google Scholar
  14. 14.
    Ozcan, A., Sule, O.G.: Multivariate temporal link prediction in evolving social networks. In: 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS), pp. 185–190 (2015)Google Scholar
  15. 15.
    Zhu, M., Zhou, Y.: Density-based link clustering algorithm for overlapping community detection. J. Comput. Res. Dev. 2520–2530 (2013)Google Scholar
  16. 16.
    Newman, M.E.J.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64, 025102 (2001)CrossRefGoogle Scholar
  17. 17.
    Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25, 211–230 (2003)CrossRefGoogle Scholar
  18. 18.
    Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009)CrossRefzbMATHGoogle Scholar
  19. 19.
    Hanley, J.A., McNeil, B.J.: A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148, 839–843 (1983)CrossRefGoogle Scholar
  20. 20.
    Zhu, M., Meng, F.R., Zhou, Y.: Density-based link clustering algorithm for overlapping community detection. 2520–2530 (2013)Google Scholar
  21. 21.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM TOIS 22, 5–53 (2004)CrossRefGoogle Scholar
  22. 22.
    McAuley, J., Leskovec, J.: Learning to discover social circles in ego networks. Adv. Neural Inf. Process. Syst. 25, 548–556 (2012)Google Scholar
  23. 23.
    Zhou, S., Mondragon, R.J.: The rich-club phenomenon in the internet topology. IEEE Common. Lett. 8, 180–182 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Chongqing Key Laboratory of Computational IntelligenceChongqing University of Posts and TelecommunicationsChongqingPeople’s Republic of China

Personalised recommendations