Soft Computing

, Volume 21, Issue 22, pp 6641–6652 | Cite as

A multiobjective discrete cuckoo search algorithm for community detection in dynamic networks

Methodologies and Application


Evolutionary clustering is a popular method for community detection in dynamic networks by introducing the concept of temporal smoothness. Some evolutionary based clustering approaches need an input parameter to control the preference degree of snapshot and temporal cost. To break the limitation of parameter selection and increase accuracy of detecting communities, we propose a multiobjective discrete cuckoo search algorithm to discover communities in dynamic networks. Firstly, ordered neighbor list method is used to encode the location of nest for population initialization. Secondly, a discrete framework of cuckoo search algorithm is proposed with a modified nest location updating strategy and abandon operator. Finally, based on the proposed discrete framework, a multiobjective discrete cuckoo search algorithm is proposed by integrating the non-dominated sorting method and the crowding distance method. Experimental results on synthetic and real networks demonstrate that the proposed algorithm is effective and has higher accuracy than other compared algorithms.


Community detection Dynamic network Multiobjective optimization Cuckoo search algorithm 



We would like to thank the anonymous referees for their many valuable suggestions and comments. This work is supported by the National Natural Science Foundation of China (Grant No. 61373123), Key Development Program for Science and Technology of Jilin Province, China (Grant No. 20150414004GH).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 554–560Google Scholar
  2. Chi Y, Song XD, Zhou D, Hino K, Tseng BL (2007) Evolutionary spectral clustering by incorporating temporal smoothness. In: Proceedings of the 13th international conference on knowledge discovery and data mining, pp 153–162Google Scholar
  3. Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):264–277CrossRefGoogle Scholar
  4. Danon L, Daz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech Theory Exp 1–10Google Scholar
  5. Folino F, Pizzuti C (2010) A multiobjective and evolutionary clustering method for dynamic networks. In: Proceedings of the international conference on advances in social networks analysis and mining, pp 256–263Google Scholar
  6. Folino F, Pizzuti C (2014) An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Trans Knowl Data Eng 26(8):1838–1852CrossRefGoogle Scholar
  7. Gong MG, Hou T, Fu B, Jiao LC (2011) A non-dominated neighbor immune algorithm for community detection in networks. In: Proceedings of the 13th annual conference on genetic and evolutionary computation (GECCO’JI), pp 1627–1634Google Scholar
  8. Gong MG, Zhang LJ, Ma JJ, Jiao LC (2012) Community detection in dynamic social networks based on multiobjective immune algorithm. J Comput Sci Technol 27(4):455–467MathSciNetCrossRefMATHGoogle Scholar
  9. Huang FL, Zhang SC, Zhu XF (2013) Discovering network community based on multi-objective optimization. J Softw 24(9):2062–2077MathSciNetMATHGoogle Scholar
  10. Kim M, Han J (2009) A particle-and-density based evolutionary clustering method for dynamic networks. Proc Int Conf Very Large Data Bases 2(1):622–633Google Scholar
  11. Lancichinetti A, Fortunato S (2009) Community detection algorithms: a comparative analysis. Phys Rev E 80:2142–2152Google Scholar
  12. Lin YR, Chi Y, Zhu S, Sundaram H, Tseng BL (2008) Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceedings of the 17th international conference on World Wide Web, pp 685–694Google Scholar
  13. Ma JJ, Liu J, Ma W, Gong MG, Jiao LC (2014) Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks. Sci World J 1–22Google Scholar
  14. Mantegna R (1992) Fast accurate algorithm for numerical simulation of levy stochastic process. Phys Rev E 49(5):451–458Google Scholar
  15. Newman MEJ, Girvan M (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99(12):7821–7826MathSciNetCrossRefMATHGoogle Scholar
  16. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):1–16CrossRefGoogle Scholar
  17. Nooy WD, Mrvar A, Batagelj V (2005) Exploratory social network analysis with pajek. Cambridge University Press, New YorkCrossRefGoogle Scholar
  18. Palla G, Barabasi AL, Vicsek T (2007) Quantifying social group evolution. Nature 446(7136):664–667CrossRefGoogle Scholar
  19. Rosvall M, Bergstrom CT (2010) Mapping change in large networks. PLoS One 5(1):1–7CrossRefGoogle Scholar
  20. Tang L, Liu H, Zhang J, Nazeri Z (2008) Community evolution in dynamic multi-mode networks. In: Proceedings of the 14th international conference on knowledge discovery and data mining, pp 677–685Google Scholar
  21. Tantipathananandh C, Berger-Wolf T, Kempe D (2007) A framework for community identification in dynamic social networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, pp 717–726Google Scholar
  22. Wang L, Zhang JY, Xu LH (2011) A dynamic network overlapping communities detecting algorithm based on local betweenness. J Shandong Univ (Nat Sci) 46(5):86–90MathSciNetGoogle Scholar
  23. Yang XS, Deb S (2009) Cuckoo search via lévy flights. In: Proceedings of world congress on nature biologically inspired computing, pp 210–214Google Scholar
  24. Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Numer Optim 1(4):330–343MATHGoogle Scholar
  25. Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624MathSciNetCrossRefMATHGoogle Scholar
  26. Zavoianu A, Lughofer E, Bramerdorfer G, Amrhein W, Klement E (2014) Decmo2–a robust hybrid multi-objective evolutionary algorithm. Soft Comput. doi: 10.1007/s00500-014-1308-7 Google Scholar
  27. Zhou X, Liu YY, Li B (2015) Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks. Phys A 436:430–442CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchunChina
  3. 3.College of MathematicsJilin UniversityChangchunChina

Personalised recommendations