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GA with k-Medoid Approach for Optimal Seed Selection to Maximize Social Influence

  • Sakshi AgarwalEmail author
  • Shikha Mehta
Conference paper
  • 26 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1087)

Abstract

In this rapidly rising field of Web, volume of online social networks has increased exponentially. This inspires the researchers to work in the area of information diffusion, i.e., spread of information through “word of mouth” effect. Information maximization is an important research problem of information diffusion, i.e., selection of k most influential nodes in the network such that they can maximize the information spread. In this paper, we proposed an influence maximization model that identifies optimal seeds to maximize the influence spread in the network. Our proposed algorithm is a hybrid approach, i.e., GA with k-medoid approach using dynamic edge strength. To analyze the efficiency of the proposed algorithm, experiments are performed on two large-scale datasets using fitness score measure. Experimental outcome illustrated 8–16% increment in influence propagation by proposed algorithm as compared to existing seed selection methods, i.e., general greedy, random, discounted degree, and high degree.

Keywords

k-Medoid Genetic algorithm Social influence Seed selection Topical affinity propagation 

References

  1. 1.
    X. Song, Y. Chi, K. Hino, B.L. Tseng, Information flow modeling based on diffusion rate for prediction and ranking, in WWW (2007), pp. 191–200Google Scholar
  2. 2.
    P. Domingos, M. Richardson, Mining the network value of customers, in KDD (2001), pp. 57–66Google Scholar
  3. 3.
    D. Kempe, J. Kleinberg, É. Tardos, Maximizing the spread of influence through a social network, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2003)Google Scholar
  4. 4.
    Y. Li, et al., Influence maximization on social graphs: a survey. IEEE Trans. Knowl. Data Eng. (2018)Google Scholar
  5. 5.
    C. Aslay et al., Influence maximization in online social networks, in Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM (2018)Google Scholar
  6. 6.
    A. Anagnostopoulos, R. Kumar, M. Mahdian, Influence and correlation in social networks, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2008)Google Scholar
  7. 7.
    W. Chen, Y. Wang, S. Yang, Efficient influence maximization in social networks, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2009)Google Scholar
  8. 8.
    R. Mittal, M.P.S. Bhatia, Identifying prominent authors from scientific collaboration multiplex social networks, in International Conference on Innovative Computing and Communications (Springer, Singapore, 2019), pp. 289–296Google Scholar
  9. 9.
    W. Chen, C. Wang, Y. Wang, Scalable influence maximization for prevalent viral marketing in large-scale social networks, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2010)Google Scholar
  10. 10.
    M.M.D. Khomami et al., Minimum positive influence dominating set and its application in influence maximization: a learning automata approach. Appl. Intell. 48(3), 570–593 (2018)CrossRefGoogle Scholar
  11. 11.
    A. Goyal, F. Bonchi, L.V.S. Lakshmanan, A data-based approach to social influence maximization. Proc. VLDB Endowment 5(1), 73–84 (2011)CrossRefGoogle Scholar
  12. 12.
    W. Chen, Y. Yuan, L. Zhang, Scalable influence maximization in social networks under the linear threshold model, in Data Mining (ICDM), 2010 IEEE 10th International Conference on. IEEE, pp. 88–97 (2010)Google Scholar
  13. 13.
    A. Kumar, S.R. Sangwan, Rumor detection using machine learning techniques on social media, in International Conference on Innovative Computing and Communications (Springer, Singapore, 2019), pp. 213–221Google Scholar
  14. 14.
    S. Agarwal, S. Mehta, Approximate shortest distance computing using k-medoids clustering. Ann Data Sci 4(4), 547–564 (2017)CrossRefGoogle Scholar
  15. 15.
    S. Agarwal and S. Mehta, Social influence maximization using genetic algorithm with dynamic probabilities, in 2018 Eleventh International Conference on Contemporary Computing (IC3) (Noida, India, 2018), pp. 1–6Google Scholar
  16. 16.
    D. Bucur, G. Iacca, Influence maximization in social networks with genetic algorithms, in European Conference on the Applications of Evolutionary Computation (Springer, Cham, 2016)CrossRefGoogle Scholar
  17. 17.
    J. Leskovec, A. Krevl, Large Network Dataset Collection (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Computer Science & Information TechnologyJaypee Institute of Information TechnogyNoidaIndia

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