Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)


As the pervasiveness of social networks increases, new NP-hard related problems become interesting for the optimization community. The objective of influence maximization is to contact the largest possible number of nodes in a network, starting from a small set of seed nodes, and assuming a model for information propagation. This problem is of utmost practical importance for applications ranging from social studies to marketing. The influence maximization problem is typically formulated assuming that the number of the seed nodes is a parameter. Differently, in this paper, we choose to formulate it in a multi-objective fashion, considering the minimization of the number of seed nodes among the goals, and we tackle it with an evolutionary approach. As a result, we are able to identify sets of seed nodes of different size that spread influence the best, providing factual data to trade-off costs with quality of the result. The methodology is tested on two real-world case studies, using two different influence propagation models, and compared against state-of-the-art heuristic algorithms. The results show that the proposed approach is almost always able to outperform the heuristics.


Influence maximization Social network Multi-objective evolutionary algorithms 



Andrea Marcelli Ph.D. program at Politecnico di Torino is supported by a fellowship from TIM (Telecom Italia Group).

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


  1. 1.
    Belluz, J., Gaudesi, M., Squillero, G., Tonda, A.: Operator selection using improved dynamic multi-armed bandit. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1311–1317. ACM (2015)Google Scholar
  2. 2.
    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, Heidelberg (2016). doi: 10.1007/978-3-319-31204-0_25CrossRefGoogle Scholar
  3. 3.
    Bucur, D., Iacca, G., Squillero, G., Tonda, A.: The impact of topology on energy consumption for collection tree protocols: an experimental assessment through evolutionary computation. Appl. Soft Comput. 16, 210–222 (2014)CrossRefGoogle Scholar
  4. 4.
    Bucur, D., Iacca, G., Squillero, G., Tonda, A.: The tradeoffs between data delivery ratio and energy costs in wireless sensor networks: a multi-objective evolutionary framework for protocol analysis. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 1071–1078. ACM (2014)Google Scholar
  5. 5.
    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, NY, USA, pp. 199–208. ACM, New York (2009)Google Scholar
  6. 6.
    Coello, C.C., Lamont, G.B., van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-objective Problems. Springer, Heidelberg (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    Corno, F., Sanchez, E., Squillero, G.: Evolving assembly programs: how games help microprocessor validation. IEEE Trans. Evol. Computat. 9(6), 695–706.
  8. 8.
    Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms, vol. 16. Wiley, Hoboken (2001)zbMATHGoogle Scholar
  9. 9.
    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
  10. 10.
    Gandini, S., Ruzzarin, W., Sanchez, E., Squillero, G., Tonda, A.: A framework for automated detection of power-related software errors in industrial verification processes. J. Electron. Test. 26(6), 689–697 (2010)CrossRefGoogle Scholar
  11. 11.
    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
  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.
    Kempe, D., Kleinberg, J.: Éva Tardos: maximizing the spread of influence through a social network. Theor. Comput. 11(4), 105–147 (2015)CrossRefzbMATHGoogle Scholar
  14. 14.
    Kim, K., McKay, R.B., Moon, B.R.: Multiobjective evolutionary algorithms for dynamic social network clustering. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO 2010, NY, USA, pp. 1179–1186. ACM, New York (2010)Google Scholar
  15. 15.
    Leskovec, J., Krevl, A.: SNAP Datasets: Stanford Large Network Dataset Collection (2016).
  16. 16.
    Liu, C., Liu, J., Jiang, Z.: A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks. IEEE Trans. Cybern. 44(12), 2274–2287 (2014)CrossRefGoogle Scholar
  17. 17.
    Neri, F., Cotta, C., Moscato, P.: Handbook of Memetic Algorithms. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 16(3), 418–430 (2012)CrossRefGoogle Scholar
  19. 19.
    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). doi: 10.1007/978-3-540-39718-2_23CrossRefGoogle Scholar
  20. 20.
    Squillero, G.: MicroGP - an evolutionary assembly program generator. Genet. Program. Evol. Mach. 6(3), 247–263 (2005)CrossRefGoogle Scholar
  21. 21.
    Tonda, A.P., Lutton, E., Reuillon, R., Squillero, G., Wuillemin, P.-H.: Bayesian network structure learning from limited datasets through graph evolution. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds.) EuroGP 2012. LNCS, vol. 7244, pp. 254–265. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-29139-5_22CrossRefGoogle Scholar
  22. 22.
    Wang, C., Chen, W., Wang, Y.: Scalable influence maximization for independent cascade model in large-scale social networks. Data Mining Knowl. Discov. 25(3), 545–576 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Zeng, Y., Liu, J.: Community detection from signed social networks using a multi-objective evolutionary algorithm. In: Handa, H., Ishibuchi, H., Ong, Y.-S., Tan, K.C. (eds.) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. PALO, vol. 1, pp. 259–270. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-13359-1_21Google Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of TwenteEnschedeThe Netherlands
  2. 2.INCASAssenThe Netherlands
  3. 3.DAUIN, Politecnico di TorinoTorinoItaly
  4. 4.INRA, UMR 782 GMPAThiverval-GrignonFrance

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