Cuckoo Search for Influence Maximization in Social Networks

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 44)

Abstract

In a social network, the influence maximization is to find out the optimal set of seeds, by which influence can be maximized at the end of diffusion process. The approaches which are already existing are greedy approaches, genetic algorithm and ant colony optimization. Eventhough these existing algorithms take more time for diffusion, they are not able to generate a good number of influenced nodes. In this paper, a Cuckoo Search Diffusion Model (CSDM) is proposed which is based on a metaheuristic approach known as the Cuckoo Search Algorithm. It uses fewer parameters than any other metaheuristic approaches. Therefore parameter tuning is an easy task for this algorithm which is the main advantage of the Cuckoo Search algorithm. Experimental results show that this model gives better results than previous works.

Keywords

Social network Influence maximization Cuckoo search 

References

  1. 1.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD, pp. 61–70 (2002)Google Scholar
  2. 2.
    Bass, F.: A new product growth model for customer durables. Manage. Sci. 15, 215–227 (1969)CrossRefMATHGoogle Scholar
  3. 3.
    Mahajan, V., Muller, E., Bass, F.M.: New product diffusion models in marketing: a review and directions for research. J. Mark. 54(1), 1–26 (1990)CrossRefGoogle Scholar
  4. 4.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Seventh International Conference on Knowledge Discovery and Data Mining (2001)Google Scholar
  5. 5.
    Kempe, D., Kleinberg, J.M., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)Google Scholar
  6. 6.
    Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: KDD, pp. 420–429 (2007)Google Scholar
  7. 7.
    Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: KDD, pp. 199–208 (2009)Google Scholar
  8. 8.
    Chen, W., Wang, Y., Yang, S.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD, pp. 1029–1038 (2010)Google Scholar
  9. 9.
    Liu, B., Cong, G., Xu, D., Zeng, Y.: Time constrained influence maximization in social networks. In: Proceedings ICDM, Washington, DC, USA, pp. 439–448 (2012)Google Scholar
  10. 10.
    Yang, W.S., Weng, S.X., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Application of the ant colony optimization algorithm to the influence-maximization problem. Int. J. Swarm Intell. Evolut. Comput. 1(1), 1–8 (2012)CrossRefGoogle Scholar
  11. 11.
    Payne, R.B., Sorenson, M.D., Klitz, K.: The Cuckoos. Oxford University Press, Oxford (2005)Google Scholar
  12. 12.
    Brown, C., Liebovitch, L.S., Glendon, R.: Levy flights in Dobe Ju/hoansi foraging patterns. Human Ecol. 35, 129–138 (2007)CrossRefGoogle Scholar
  13. 13.
    Pavlyukevich, I.: Levy flights, non-local search and simulated annealing. J. Comput. Phys. 226, 1830–1844 (2007)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Pavlyukevich, I.: Cooling down Levy flights. J. Phys. A: Math. Theory 40, 12299–12313 (2007)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Barthelemy, P., Bertolotti, J., Wiersma, D.S.: A Levy flight for light. Nature 453, 495–498 (2008)CrossRefGoogle Scholar
  16. 16.
    Reynolds, A.M., Frye, M.A.: Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search. PLoS ONE 2, e354 (2007)CrossRefGoogle Scholar
  17. 17.
    Yang, X.-S., Deb, S.: Cuckoo search via Levy flights. In: Proceedings of the Nabic—World Congress on Nature and Biologically Inspire Computing, pp. 210–214 (2009)Google Scholar
  18. 18.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. University of Cambrige, Luniver Press, United Kingdom (2010)Google Scholar
  19. 19.
    Yang, X.S., Deb, S.: A engineering optimisation by cuckoo search. Int. J. Math. Modell. Numer. Optim. 1(4), 330343 (2010)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology KarnatakaSurathkalIndia

Personalised recommendations