A Knowledge Based Framework for Link Prediction in Social Networks

  • Pooya Moradian ZadehEmail author
  • Ziad Kobti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9616)


Social networks have a dynamic nature so their structures change over time. In this paper, we propose a new evolutionary method to predict the state of a network in the near future by extracting knowledge from its current structure. This method is based on the fact that social networks consist of communities. Observing current state of a given network, the method calculates the probability of a relationship between each pair of individuals who are not directly connected to each other and estimate the chance of being connected in the next time slot. We have tested and compared the method on one synthetic and one large real dataset with 117 185 083 edges. Results show that our method can predict the next state of a network with a high rate of accuracy.


Social networks Link prediction Cultural algorithm Evolutionary algorithm Knowledge Community detection 



This work is partially supported by a Cross-Border Institute (CBI) Research Grant.


  1. 1.
    Bliss, C.A., Frank, M.R., Danforth, C.M., Dodds, P.S.: An evolutionary algorithm approach to link prediction in dynamic social networks. J. Comput. Sci. 5(5), 750–764 (2014)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Chen, B., Chen, L.: A link prediction algorithm based on ant colony optimization. Appl. Intell. 41(3), 694–708 (2014)CrossRefGoogle Scholar
  3. 3.
    Fire, M., Tenenboim, L., Lesser, O., Puzis, R., Rokach, L., Elovici, Y.: Link prediction in social networks using computationally efficient topological features. In: 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), PASSAT/SocialCom 2011, Privacy, Security, Risk and Trust (PASSAT), Boston, MA, USA, pp. 73–80, 9–11 October 2011Google Scholar
  4. 4.
    Hasan, M.A., Zaki, M.J.: A survey of link prediction in social networks. In: Aggarwal, C.C. (ed.) Social Network Data Analytics, pp. 243–275. Springer, USA (2011)CrossRefGoogle Scholar
  5. 5.
    Leskovic, J., Krevl, A.: SNAP datasets. In: SNAP Datasets: Stanford Large Network Dataset Collection.
  6. 6.
    Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Physica A 390(6), 1150–1170 (2011)CrossRefGoogle Scholar
  7. 7.
    Newman, M.: Detecting community structure in networks. Eur. Phys. J. B 38(2), 321–330 (2004)CrossRefGoogle Scholar
  8. 8.
    Park, Y., Song, M.: A genetic algorithm for clustering problems. In: Koza, J.R., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M.H., Goldberg, D.E., Iba, H., Riolo, R. (eds.) Proceedings of the Third Annual Conference on Genetic Programming, pp. 568–575. Morgan Kaufmann, University of Wisconsin, Madison, Wisconsin, 22–25 July 1998Google Scholar
  9. 9.
    Qiu, B., He, Q., Yen, J.: Evolution of node behavior in link prediction. In: Burgard, W., Roth, D. (eds.) Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, California, USA, 7–11 August 2011Google Scholar
  10. 10.
    Reynolds, R.G.: An introduction to cultural algorithms. In: Sebald, A.V., Fogel, L.J. (eds.) Proceedings of the Third Annual Conference Evolutionary Programming, pp. 131–139. World Scientific Press, San Diego, CA, 24–26 February 1994Google Scholar
  11. 11.
    Sherkat, E., Rahgozar, M., Asadpour, M.: Structural link prediction based on ant colony approach in social networks. Physica A 419, 80–94 (2015)CrossRefGoogle Scholar
  12. 12.
    Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015)CrossRefGoogle Scholar
  13. 13.
    Zadeh, P.M., Kobti, Z.: A multi-population cultural algorithm for community detection in social networks. Procedia Comput. Sci. 52, 342–349 (2015). Shakshuki, E.M. (ed.) Proceedings of the 6th International Conference on Ambient Systems, Networks and Technologies (ANT 2015), the 5th International Conference on Sustainable Energy Information Technology (SEIT-2015), London, UK, 2–5 June 2015CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of WindsorWindsorCanada

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