Skip to main content
Log in

A discrete modified fireworks algorithm for community detection in complex networks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In the last decade, several algorithms have been proposed to solve the problem of community detection in complex networks. Many of them are based on swarm intelligence and evolutionary algorithms. Most of these algorithms use the modularity density as a fitness function to maximize. However, these algorithms attempt to find the best solution without taking into consideration the structure of the network. In this paper, a new discrete modified Fireworks Algorithm (FWA) has been developed to solve the problem of community detection. A new initialization strategy and new mutation strategies are proposed, based on the label propagation strategy to enhance the algorithm and to speed up its convergence. The proposed algorithm has been evaluated on real-world and synthetic networks. Experimental results compared with three other known algorithms show the effectiveness of using our proposed algorithm for solving the problem of detecting communities in complex networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. Books about US Politics: http://networkdata.ics.uci.edu/data.php?d=polbooks

References

  1. Brandes U, Delling D, Gaertler M, Görke R, Hoefer M, Nikoloski Z, Wagner D (2006) Maximizing modularity is hard. arXiv preprint physics/0608255

  2. Cai Q, Ma L, Gong M, Tian D (2014) A survey on network community detection based on evolutionary computation. Int J Bio-Inspired Comput

  3. Cai Q, Gong M, Ma L, Jiao L (2015a) A novel clonal selection algorithm for community detection in complex networks. Comput Intell 31(3):442–464

    Article  MathSciNet  Google Scholar 

  4. Cai Q, Gong M, Ma L, Ruan S, Yuan F, Jiao L (2015b) Greedy discrete particle swarm optimization for large-scale social network clustering. Inf Sci 316:503–516

    Article  Google Scholar 

  5. Clauset A, Newman ME, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):066,111

    Article  Google Scholar 

  6. Ding K, Zheng S, Tan Y (2013) A gpu-based parallel fireworks algorithm for optimization. In: Proceedings of the 15th annual conference on Genetic and evolutionary computation, ACM, pp 9–16

  7. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174

    Article  MathSciNet  Google Scholar 

  8. Fortunato S, Barthelemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci 104 (1):36–41

    Article  Google Scholar 

  9. García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the cec2005 special session on real parameter optimization. J Heuristics 15(6):617–644

    Article  MATH  Google Scholar 

  10. Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  11. Gog A, Dumitrescu D, Hirsbrunner B (2007) Community detection in complex networks using collaborative evolutionary algorithms. In: Advances in Artificial Life. Springer, pp 886–894

  12. Gong M, Fu B, Jiao L, Du H (2011) Memetic algorithm for community detection in networks. Phys Rev E 84(5):056,101

    Article  Google Scholar 

  13. Gong M, Cai Q, Li Y, Ma J (2012) An improved memetic algorithm for community detection in complex networks. In: Evolutionary computation (CEC), 2012 IEEE Congress on IEEE, pp 1–8

  14. Gong M, Cai Q, Chen X, Ma L (2014) Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Trans Evol Comput 18(1):82–97

    Article  Google Scholar 

  15. Guimera R, Sales-Pardo M, Amaral LAN (2004) Modularity from fluctuations in random graphs and complex networks. Phys Rev E 70(2):025,101

    Article  Google Scholar 

  16. Guoqiang C, Xiaofang G (2010) A genetic algorithm based on modularity density for detecting community structure in complex networks. In: Computational intelligence and security (CIS), 2010 International Conference on IEEE, pp 151–154

  17. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046,110

    Article  Google Scholar 

  18. Li J, Zheng S, Tan Y (2014) Adaptive fireworks algorithm. In: Evolutionary computation (CEC), 2014 IEEE Congress on IEEE, pp 3214–3221

  19. Li Z, Zhang S, Wang RS, Zhang XS, Chen L (2008) Quantitative function for community detection. Phys Rev E 77(3):036,109

    Article  Google Scholar 

  20. Lipczak M, Milios E (2009) Agglomerative genetic algorithm for clustering in social networks. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, ACM, pp 1243–1250

  21. Liu X, Li D, Wang S, Tao Z (2007) Effective algorithm for detecting community structure in complex networks based on ga and clustering. In: Computational Science–ICCS 2007. Springer, pp 657–664

  22. Lusseau D (2003) The emergent properties of a dolphin social network. Proc R Soc Lond B Biol Sci 270(Suppl 2):S186– S188

    Article  Google Scholar 

  23. Lusseau D, Schneider K, Boisseau OJ, Haase P, Slooten E, Dawson SM (2003) The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav Ecol Sociobiol 54(4):396–405

    Article  Google Scholar 

  24. Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582

    Article  Google Scholar 

  25. Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69 (2):026,113

    Article  Google Scholar 

  26. Pang-Ning T, Steinbach M, Kumar V, et al. (2006) Introduction to data mining. In: Library of congress, vol 74

  27. Pizzuti C (2008) Ga-net: A genetic algorithm for community detection in social networks. In: Parallel Problem Solving from Nature–PPSN X. Springer, pp 1081–1090

  28. Pizzuti C (2012) A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans Evol Comput 16(3):418–430

    Article  Google Scholar 

  29. Rahmani A, Amine A, Hamou RM, Rahmani ME, Bouarara HA (2015) Privacy preserving through fireworks algorithm based model for image perturbation in big data. International Journal of Swarm Intelligence Research (IJSIR) 6(3):41– 58

    Article  Google Scholar 

  30. Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci 105(4):1118–1123

    Article  Google Scholar 

  31. Shi Z, Liu Y, Liang J (2009) Pso-based community detection in complex networks. In: Knowledge acquisition and modeling, 2009. KAM’09. Second international symposium on, IEEE, vol 3, pp 114–119

  32. Singhal A (2001) Modern information retrieval: a brief overview. IEEE Data Eng Bull 24(4):35–43

    Google Scholar 

  33. Suaris PR, Kedem G (1988) An algorithm for quadrisection and its application to standard cell placement. IEEE Trans Circuits Syst 35(3):294–303

    Article  Google Scholar 

  34. Tan Y (2015) Discrete firework algorithm for combinatorial optimization problem. In: Fireworks Algorithm. Springer, pp 209–226

  35. Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: Advances in Swarm Intelligence. Springer, pp 355–364

  36. Tasgin M, Herdagdelen A, Bingol H (2007) Community detection in complex networks using genetic algorithms. arXiv preprint arXiv:07110491

  37. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83

    Article  Google Scholar 

  38. Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res:452–473

  39. Zhang B, Zhang M, Zheng YJ (2014) Improving enhanced fireworks algorithm with new gaussian explosion and population selection strategies. In: Advances in Swarm Intelligence. Springer, pp 53–63

  40. Zheng S., Janecek A., Tan Y (2013a) Enhanced fireworks algorithm. In: Evolutionary Computation (CEC), 2013 IEEE Congress on, IEEE, pp 2069–2077

  41. Zheng S, Janecek A, Li J, Tan Y (2014) Dynamic search in fireworks algorithm. In: Evolutionary computation (CEC), 2014 IEEE Congress on, IEEE, pp 3222–3229

  42. Zheng YJ, Song Q, Chen SY (2013b) Multiobjective fireworks optimization for variable-rate fertilization in oil crop production. Appl Soft Comput 13(11):4253–4263. doi:10.1016/j.asoc.2013.07.004. http://www.sciencedirect.com/science/article/pii/S1568494613002305

  43. Zheng YJ, Xu XL, Ling HF, Chen SY (2015) A hybrid fireworks optimization method with differential evolution operators. Neurocomputing 148:75 – 82. doi:10.1016/j.neucom.2012.08.075. http://www.sciencedirect.com/science/article/pii/S092523121400931X

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed Guendouz.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guendouz, M., Amine, A. & Hamou, R.M. A discrete modified fireworks algorithm for community detection in complex networks. Appl Intell 46, 373–385 (2017). https://doi.org/10.1007/s10489-016-0840-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-016-0840-9

Keywords

Navigation