Improving Multi-objective Evolutionary Influence Maximization in Social Networks

  • Doina Bucur
  • Giovanni Iacca
  • Andrea Marcelli
  • Giovanni Squillero
  • Alberto Tonda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10784)

Abstract

In the context of social networks, maximizing influence means contacting the largest possible number of nodes starting from a set of seed nodes, and assuming a model for influence propagation. The real-world applications of influence maximization are of uttermost importance, and range from social studies to marketing campaigns. Building on a previous work on multi-objective evolutionary influence maximization, we propose improvements that not only speed up the optimization process considerably, but also deliver higher-quality results. State-of-the-art heuristics are run for different sizes of the seed sets, and the results are then used to initialize the population of a multi-objective evolutionary algorithm. The proposed approach is tested on three publicly available real-world networks, where we show that the evolutionary algorithm is able to improve upon the solutions found by the heuristics, while also converging faster than an evolutionary algorithm started from scratch.

Keywords

Influence maximization Social network Multi-objective evolutionary algorithms Seeding 

Notes

Acknowledgments

This article is based upon work from COST Action CA15140 ‘Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)’ supported by the COST Agency.

References

  1. 1.
    Hersh, E.D.: Hacking the Electorate: How Campaigns Perceive Voters. Cambridge University Press, Cambridge (2015)CrossRefGoogle Scholar
  2. 2.
    Kreiss, D.: Prototype Politics: Technology-intensive Campaigning and the Data of Democracy. Oxford University Press, Oxford (2016)CrossRefGoogle Scholar
  3. 3.
    Grassegger, H., Krogerus, M.: The data that turned the world upside down. Luettu 28 (2017). Luettavissa: http://motherboard.vice.com/read/big-data-cambridge-analytica-brexit-trump
  4. 4.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. Theory Comput. 11(4), 105–147 (2015)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Richardson, M., Agrawal, R., Domingos, P.: Trust management for the semantic web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-39718-2_23 CrossRefGoogle Scholar
  6. 6.
    Bucur, D., Iacca, G.: Influence maximization in social networks with genetic algorithms. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9597, pp. 379–392. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-31204-0_25 CrossRefGoogle Scholar
  7. 7.
    Bucur, D., Iacca, G., Marcelli, A., Squillero, G., Tonda, A.: Multi-objective evolutionary algorithms for influence maximization in social networks. In: Squillero, G., Sim, K. (eds.) EvoApplications 2017. LNCS, vol. 10199, pp. 221–233. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55849-3_15 CrossRefGoogle Scholar
  8. 8.
    Goldenberg, J., Libai, B., Muller, E.: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12(3), 211–223 (2001)CrossRefGoogle Scholar
  9. 9.
    Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 199–208. ACM, New York (2009)Google Scholar
  10. 10.
    Wang, X., Zhang, X., Zhao, C., Yi, D.: Maximizing the spread of influence via generalized degree discount. In: PloS one (2016)Google Scholar
  11. 11.
    Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 420–429, August 2007Google Scholar
  12. 12.
    Jiang, Q., Song, G., Cong, G., Wang, Y., Si, W., Xie, K.: Simulated annealing based influence maximization in social networks. In: Burgard, W., Roth, D. (eds.) AAAI. AAAI Press (2011)Google Scholar
  13. 13.
    Coello, C.A.C., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-objective Problems, vol. 242. Springer, New York (2002).  https://doi.org/10.1007/978-0-387-36797-2 CrossRefMATHGoogle Scholar
  14. 14.
    Squillero, G.: MicroGP - an evolutionary assembly program generator. Genet. Program. Evolvable Mach. 6(3), 247–263 (2005)CrossRefGoogle Scholar
  15. 15.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.EEMCSUniversity of TwenteEnschedeThe Netherlands
  2. 2.Integrated Signal Processing SystemsRWTH Aachen UniversityAachenGermany
  3. 3.DAUINPolitecnico di TorinoTorinoItaly
  4. 4.INRA, UMR 782 GMPAThiverval-GrignonFrance

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