Skip to main content

Advertisement

SpringerLink
Log in

An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper improved Cuckoo Search Optimization (CSO) algorithm with a Genetic Algorithm (GA) for community detection in complex networks. CSO algorithm has problems such as premature convergence, delayed convergence, and getting trapped in the local trap. GA has been quite successful in terms of community detection in complex networks to increase exploration and exploitation. GA operators have been used dynamically in order to increase the speed and accuracy of the CSO. The number of populations is dynamically adjusted based on the amount of exploration and exploitation. Modularity objective function (Q) and Normalized Mutual Information (NMI) is used as an optimization function. It was carried out on six types of real complex networks. The proposed algorithm was tested with GA, Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), and CSO, with different iterations in modularity and NMI criteria. The results show that in most comparisons, the proposed algorithm has been more successful than the basic comparative algorithms, and it has proven its superiority in terms of modularity and NMI. The proposed algorithm performed an average of 54% better in modularity and 88% in NMI than other algorithms. It performed on average in modularity criteria 84.3%, 58.8%, 33.7% and 38.8%, respectively, compared to CSO, ABS, GWO and GA algorithms, and in terms of NMI index, 188.7%, 39.1%, 52.3% and 73.8%, respectively in CSO, ABS, GWO and GA algorithms performed better.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Abdollahzadeh B, Gharehchopogh FS (2021) A multi-objective optimization algorithm for feature selection problems. Eng Comput: 1–19

  2. Adamic LA, Glance N (2005) The political blogosphere and the 2004 US election: divided they blog. In: Proceedings of the 3rd international workshop on Link discovery. ACM

  3. Bai L et al (2017) Fast graph clustering with a new description model for community detection. Inf Sci 388:37–47

    Article  Google Scholar 

  4. Bai L et al (2018) A novel community detection algorithm based on simplification of complex networks. Knowl Based Syst 143:58–64

    Article  Google Scholar 

  5. Dilmaghani S et al (2021)Community Detection in Complex Networks: A Survey on Local Approaches. in Intelligent Information and Database Systems Springer International Publishing, Cham

    Google Scholar 

  6. Dong S (2020) Improved label propagation algorithm for overlapping community detection. Computing 102(10):2185–2198

    Article  MathSciNet  Google Scholar 

  7. Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm Evol Comput 48:1–24

    Article  Google Scholar 

  8. Gharehchopogh FS, Shayanfar H, Gholizadeh H (2019) A comprehensive survey on symbiotic organisms search algorithms. Artif Intell Rev:1–48

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

  10. Guendouz M, Amine A, Hamou RM (2017) A discrete modified fireworks algorithm for community detection in complex networks. Appl Intell 46(2):373–385

    Article  Google Scholar 

  11. Guerrero M et al (2017) Adaptive community detection in complex networks using genetic algorithms. Neurocomputing 266:101–113

    Article  Google Scholar 

  12. Jin H, Wang S, Li C (2013) Community detection in complex networks by density-based clustering. Phys A 392(19):4606–4618

    Article  Google Scholar 

  13. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  14. Kaur S et al (2016) Comparative analysis of quality metrics for community detection in social networks using genetic algorithm. Neural Netw World 26(6):625

    Article  Google Scholar 

  15. Kim P, Kim S (2017) Detecting community structure in complex networks using an interaction optimization process. Phys A 465:525–542

    Article  Google Scholar 

  16. Knuth DE (1993) The Stanford GraphBase: a platform for combinatorial computing. AcM Press, New York

    MATH  Google Scholar 

  17. Li Z, Liu J (2016) A multi-agent genetic algorithm for community detection in complex networks. Phys A 449:336–347

    Article  Google Scholar 

  18. Lusseau D et al (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 

  19. Ma H, Yang H, Zhou K, Zhang L, Zhang X (2020) A local-to-globalscheme-based multi-objective evolutionary algorithm for overlapping community detection on large-scale complex networks. Neural Comput Appl 33(10):5135–5149

    Article  Google Scholar 

  20. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  21. Newman ME (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(3):036104

    Article  MathSciNet  Google Scholar 

  22. Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113

    Article  Google Scholar 

  23. Pizzuti C (2008) Ga-net: A genetic algorithm for community detection in social networks. In: International Conference on Parallel Problem Solving from Nature. Springer, Berlin

  24. Rabani H, Soleimanian Gharehchopogh F (2019) An Optimized Firefly Algorithm based on Cellular Learning Automata for Community Detection in Social Networks. J Adv Comput Res 10(3):13–30

    Google Scholar 

  25. Rahimi S, Abdollahpouri A, Moradi P (2018) A multi-objective particle swarm optimization algorithm for community detection in complex networks. Swarm Evol Comput 39:297–309

    Article  Google Scholar 

  26. Rahnema N, Gharehchopogh FS (2020) An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering. Multimed Tools Appl 79(43):32169–32194

    Article  Google Scholar 

  27. Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11(8):5508–5518

    Article  Google Scholar 

  28. Ranjbar A, Maheswaran M (2014) Using community structure to control information sharing in online social networks. Comput Commun 41:11–21

    Article  Google Scholar 

  29. Said A et al (2018) CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks. Appl Soft Comput 63:59–70

    Article  Google Scholar 

  30. Sedghpour AS, Nikanjam A (2017) Overlapping community detection in social networks using a quantum-based genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM

  31. Shahabi Sani N, Manthouri M, Farivar F (2020) A multi-objective ant colony optimization algorithm for community detection in complex networks. J Ambient Intell Humaniz Comput 11(1):5–21

    Article  Google Scholar 

  32. Shayanfar H, Gharehchopogh FS (2018) Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728–746

    Article  Google Scholar 

  33. Shi C et al (2012)Multi-objective community detection in complex networks. Appl Soft Comput 12(2):850–859

    Article  Google Scholar 

  34. Sun H et al (2018) A parallel self-organizing overlapping community detection algorithm based on swarm intelligence for large scale complex networks. Futur Gener Comput Syst 89:265–285

    Article  Google Scholar 

  35. Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85

    Article  Google Scholar 

  36. Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33(4):452–473

    Article  Google Scholar 

  37. Zhou X et al (2017) A multi-objective discrete cuckoo search algorithm for community detection in dynamic networks. Soft Comput 21(22):6641–6652

    Article  Google Scholar 

  38. Zhou H et al (2017) A graph clustering method for community detection in complex networks. Phys A 469:551–562

    Article  Google Scholar 

  39. Zhou H, Zhang Y, Li J (2018) An overlapping community detection algorithm in complex networks based on information theory. Data Knowl Eng 117:183–194

    Article  Google Scholar 

  40. Zhang W, Zhang R, Shang R, Li J, Jiao L (2018) Application of natural computation inspired method in community detection. Phys A: Stat Mech Appl 515:130–150

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Saeid Talebpour Shishavan & Farhad Soleimanian Gharehchopogh

Authors
  1. Saeid Talebpour Shishavan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farhad Soleimanian Gharehchopogh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farhad Soleimanian Gharehchopogh.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shishavan, S.T., Gharehchopogh, F.S. An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks. Multimed Tools Appl 81, 25205–25231 (2022). https://doi.org/10.1007/s11042-022-12409-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12409-x

Keywords

Search

Navigation