Advertisement

A Hybrid Bat Algorithm for Community Detection in Social Networks

  • Seema RaniEmail author
  • Monica Mehrotra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

In this work, a hybrid optimization method is proposed for dealing with the community discovery problem in social networks relying on the bat algorithm. The proposed method hybrids discrete bat algorithm with Tabu search for enhancing the quality of solution in contrast to discrete bat algorithm. The Tabu search is a neighborhood search based method. The local search capability of bat algorithm is improved by introducing the Tabu search strategy. The recommended hybrid approach is tested on a few real-world networks and synthetic benchmark network. The obtained results are very promising and comparable as well. The results are compared with existing algorithms which demonstrate that the proposed method enhances the quality of the obtained solution.

Keywords

Bat algorithm Tabu search Community detection Social networks 

References

  1. 1.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. B 38(2), 321–330 (2004)CrossRefGoogle Scholar
  3. 3.
    Danon, L., Díaz-Guilera, A., Duch, J., Diaz-Guilera, A., Arenas, A.: Comparing community structure identification. J. Stat. 09008, 10 (2005)Google Scholar
  4. 4.
    Brandes, U., et al.: On modularity clustering. IEEE Trans. Knowl. Data Eng. 20(2), 172–188 (2008)CrossRefGoogle Scholar
  5. 5.
    Tasgin, M., Bingol, H.: Community Detection in Complex Networks using Genetic Algorithm, arXiv Prepr, p. 6 (2006)Google Scholar
  6. 6.
    Shi, Z., Liu, Y., Liang, J.: PSO-based community detection in complex networks. In: 2009 Second International Symposium on Knowledge Acquisition and Modeling, pp. 114–119 (2009)Google Scholar
  7. 7.
    Chen, Y., Qiu, X.: Detecting community structures in social networks with particle swarm optimization. In: 2nd Communications in Computer and Information Science, vol. 401, pp. 266–275 (2013)Google Scholar
  8. 8.
    Guimerà, R., Nunes Amaral, L.A.: Functional cartography of complex metabolic networks. Nature 433(7028), 895–900 (2005)CrossRefGoogle Scholar
  9. 9.
    Yang, X.S.: A new metaheuristic bat-inspired algorithm. Stud. Comput. Intell. 284, 65–74 (2010)zbMATHGoogle Scholar
  10. 10.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  11. 11.
    Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27(4), 1053–1073 (2016)CrossRefGoogle Scholar
  12. 12.
    Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)CrossRefGoogle Scholar
  13. 13.
    Mirjalili, S., Mirjalili, S.M., Yang, X.S.: Binary bat algorithm. Neural Comput. Appl. 25(3–4), 663–681 (2013)Google Scholar
  14. 14.
    Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108 (1997)Google Scholar
  15. 15.
    Holland, J.: Adaptation in natural and artificial systems: an introductory analysis with application to biology. Control Artif. Intell. (1975)Google Scholar
  16. 16.
    Song, A., Li, M., Ding, X., Cao, W., Pu, K.: Community detection using discrete bat algorithm. Int. J. Comput. Sci. 43(1), 37–43 (2016)Google Scholar
  17. 17.
    Yang, X.S.: Bat algorithm and cuckoo search: a tutorial. Stud. Comput. Intell. 427, 421–434 (2013)zbMATHGoogle Scholar
  18. 18.
    Kora, P., Kalva, S.R.: Improved bat algorithm for the detection of myocardial infarction. Springerplus 4(1), 666 (2015)CrossRefGoogle Scholar
  19. 19.
    Salma, U.M.: A binary bat inspired algorithm for the classification of breast cancer data. Int. J. Soft Comput. Artif. Intell. Appl. 53(2), 1–21 (2016)Google Scholar
  20. 20.
    Huang, X., Zeng, X., Han, R.: Dynamic inertia weight binary bat algorithm with neighborhood search. Comput. Intell. Neurosci. 2017 (2017).  https://doi.org/10.1155/2017/3235720
  21. 21.
    Glover, F.: Tabu search: a tutorial. Interfaces 20(4), 74–94 (1990)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Cai, Q., Gong, M., Shen, B., Ma, L., Jiao, L.: Discrete particle swarm optimization for identifying community structures in signed social networks. Neural Netw. 58, 4–13 (2014)CrossRefGoogle Scholar
  23. 23.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)Google Scholar
  24. 24.
    Newman, M.E.J.: Community detection and graph partitioning, no. 2 (2013)Google Scholar
  25. 25.
    Mamun-Ur-Rashid Khan, M., Asadujjaman, M.: A tabu search approximation for finding the shortest distance using traveling salesman problem. IOSR J. Math. 12(05), 80–84 (2016)CrossRefGoogle Scholar
  26. 26.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977). http://www.jstor.org/stable/3629752
  27. 27.
    Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  28. 28.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 46110 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceJamia Millia IslamiaNew DelhiIndia

Personalised recommendations