Skip to main content
SpringerLink
Log in

A multi-objective bat algorithm for community detection on dynamic social networks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Many evolutionary algorithms have been proposed to deal with the problem of community detection in social dynamic networks. Some algorithms need to fix parameters in advance; others use a random process to generate the initial population and to apply the algorithm operators. These drawbacks increase the search space and cause a high spatial and temporary complexity. To overcome these weaknesses, we propose in this paper a novel multi-objective Bat Algorithm that uses Mean Shift algorithm to generate the initial population, to obtain solutions of high quality. In our proposal, Bat Algorithm simultaneously optimizes the modularity density and the normalized mutual information of the solutions as objective functions. The operators of the algorithm are applied to the problem of community detection in social dynamic networks by giving another sense to the velocity, frequency, loudness and the pulse rate of natural Bat. The algorithm keeps the principal of the Mean Shift algorithm to generate new solution and avoid the random process by defining a new mutation operator. The algorithm does not need to the non-dominated sorted approach or the crowding distance, but it attributes a weight to each objective function. The method is tested on artificial and real dynamic networks and the experiments show satisfactory results in terms of normalized mutual information, modularity and error rate.

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.

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

Similar content being viewed by others

References

  1. Xie, J., B. K. Szymanski, and X. Liu (2011) Slpa: Uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In 2011 ieee 11th international conference on data mining workshops pp. 344–349

  2. Bello-Orgaz G, Camacho D (2014) Evolutionary clustering algorithm for community detection using graph-based information. In: 2014 IEEE Congress on Evolutionary Computation Beijing, CEC 2014, pp 930–937

  3. Belkhiri Y, Kamel N, Drias H, Yahiaoui S (2017) Bee Swarm Optimization for Community Detection in Complex Network. In: Rocha Á, Correia A, Adeli H, Reis L, Costanzo S (eds) Recent Advances in Information Systems and Technologies. WorldCIST 2017. Advances in intelligent systems and computing, vol 570. Springer, Cham

    Google Scholar 

  4. Imane M, Kamel N (2018) Overlapping community detection with a novel hybrid metaheuristic optimization algorithm. International Journal of Data Mining, Modelling and Management

  5. Duan D, Li Y, Li R, Lu Z (2012) Incremental K-clique clustering in dynamic social networks. Artif Intell Rev 38:129–147

    Article  Google Scholar 

  6. Enugala R, Rajamani L, Ali K, Kurapati S (2015) Community detection in dynamic social networks: a survey. International Journal of Research and Applications 2(6):278–285

    Article  Google Scholar 

  7. Liu Q et al (2016) Evolutionary link community structure discovery in dynamic weighted networks. Physica A

  8. Lin Y-R, Chi Y, Zhu S et al (2008) A framework for analyzing communities and their evolutions in dynamic networks. In: proceedings of the 17th international conference on world wide web, pp 685–694

    Chapter  Google Scholar 

  9. Folino F, Pizzuti C (2014) An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Trans Knowl Data Eng 26:1838–1852

    Article  Google Scholar 

  10. Zhou X, Liu Y, Li B, Sun G (2015) Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks. Physica A: Statistical Mechanics and its Applications 436:430–442

    Article  MATH  Google Scholar 

  11. Niu X, Si W, Wu CQ (2017) A label-based evolutionary computing approach to dynamic community detection. Comput Commun 108:110–122

    Article  Google Scholar 

  12. Zhang X-K, Ren J, Song C, Jia J, Zhang Q (2017) Label propagation algorithm for community detection based on node importance and label influence. In: Physics Letters A, vol 381, No. 33, pp 2691–2698

    Google Scholar 

  13. Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69(6):066133+. https://doi.org/10.1103/physreve.69.066133

    Article  Google Scholar 

  14. V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre (2008) Fast unfolding of communities in large networks. J Stat Mech: Theory Exp, 2008 (10), p. P10008

  15. 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 

  16. Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76(3):036106

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Ji J, Song X, Liu C, Zhang X (2013) Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks. Physica A: Statistical Mechanics and its Applications 392(15):3260–3272

    Article  Google Scholar 

  19. Lili FU et al (2014) Research on Spectral Clustering. Appl Mech Mater 687–691:1350–1353

  20. Hu Y, Yang B, Lv C (2016) A local dynamic method for tracking communities and their evolution in dynamic networks. Knowl-Based Syst 110:176–190

    Article  Google Scholar 

  21. Chang-Dong W, Jian-Huang L, Philip SY (2014) NEIWalk: Community Discovery in Dynamic Content Based Networks. IEEE Trans Knowl Data Eng 26 No. 7

  22. Nagehan I, Sule GO (2013) Community Event Prediction in Dynamic Social Networks. In: proceedings 12th IEEE International Conference on Machine Learning and Applications, pp 191–196

    Google Scholar 

  23. Asur S, Parthasarathy S, Ucar D (2009) An event-based framework for characterizing the evolutionary behavior of interaction graphs. Trans Knowl Discov Data, ACM 3:16

    Article  Google Scholar 

  24. Peizhuo Wang et al (2017). Dynamic community detection based on network structural perturbation and topological similarity. J Stat Mech Theory Exp 2017. https://doi.org/10.1088/1742-5468/2017/1/013401

  25. Attea B.A (2016) A new multi-objective evolutionary framework for community mining in dynamic social networks. Swarm and Evolutionary

  26. Gao C et al (2018) Multiobjective discrete particle swarm optimization for community detection in dynamic networks. EPL 122(2):28001

    Article  Google Scholar 

  27. Augusto O, Fouad B, Caro S (2012) A new method for decision making in multi-objective optimization problems. Pesqui Oper 32(2):331–369

  28. Yammani C, Maheswarapu S, Matam SK (2016) A multi-objective shuffled bat algorithm for optimal placement and sizing of multi distributed generations with different load models. Int J Electr Power Energy Syst 79:120–131

    Article  Google Scholar 

  29. Tharakeshwar TK, Seetharamu KN, Durga Prasad B (2017) Multi-objective optimization using bat algorithm for shell and tube heat exchangers. Appl Therm Eng 110:1029–1038

    Article  Google Scholar 

  30. Yang N-C, Le M-D (2015) Optimal design of passive power filters based on multi-objective bat algorithm and pareto front. Appl Soft Comput 35:257–266 ISSN 1568-4946

    Article  Google Scholar 

  31. Liang H, Liu Y, Li F, Shen Y (2018) A multiobjective hybrid bat algorithm for combined economic/emission dispatch. Int J Electr Power Energy Syst 101:103–115 ISSN 0142-0615

    Article  Google Scholar 

  32. Yang XS (2010) A New Metaheuristic Bat-Inspired Algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol 284. Springer, Berlin, Heidelberg

  33. Laamari MA, Kamel N (2014) A hybrid bat based feature selection approach for intrusion detection. In: Pan L, Păun G, Pérez-Jiménez MJ, Song T (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg

    Google Scholar 

  34. Heraguemi KE, Kamel N, Drias H (2014) Association rule mining based on bat algorithm. In: Pan L, Păun G, Pérez-Jiménez MJ, Song T (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg

    Google Scholar 

  35. Heraguemi K, Kamel N, Drias H (2018) Multi-objective bat algorithm for mining numerical association rules. International Journal of Bio-Inspired Computation 11(239). https://doi.org/10.1504/IJBIC.2018.10013987

  36. Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscip Optim 26:369–395

    Article  MathSciNet  MATH  Google Scholar 

  37. Yang XS (2011) Bat algorithm for multiobjective optimization. Int J Bio-Inspired Computation 3(5):267–274

    Article  Google Scholar 

  38. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

  39. Shang R et al (2016) A multiobjective evolutionary algorithm to find community structures based on affinity propagation. Physica A 453:203–227

    Article  Google Scholar 

  40. Hu Y-Q, Li M-H et al (2008) Community detection by signaling on complex networks. Phys Rev E 78

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

    Google Scholar 

  42. MacKay DJC (2002) Information theory, inference and learning algorithms. Cambridge University Press, Cambridge

    Google Scholar 

  43. Folino F, Pizzuti C (2014) An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Trans Knowl Data Eng Vol. 99, No. 8

  44. You T, Cheng H-M, Ning Y-Z, Shia B-C, Zhang Z-Y (2016) Community detection in complex networks using density-based clustering algorithm and manifold learning. Physica A: Statistical Mechanics and Its Applications 464:221–230

    Article  Google Scholar 

  45. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69

  46. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E Stat Nonlinear Soft Matter Phys 78:046110. https://doi.org/10.1103/PhysRevE.78.046110

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

    Article  Google Scholar 

  48. Pourkazemi M, Keyvanpour MR (2017) Community detection in social network by using a multi-objective evolutionary algorithm. Intelligent Data Analysis 21:385–409

    Article  Google Scholar 

  49. Gong M, Ma L, Zhang Q, Jiao L (2012) Community detection in networks by using multiobjective evolutionary algorithm with decomposition. Physica A, Elsevier 391:4050–4060

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LRIA Laboratory, Department of Computer Science, Universite de Sciences et de la Technology Houari Boumediene, BP 32 El alia 16111 Bab ezzouar, Algiers, Algeria

    Imane Messaoudi

  2. Faculty of Sciences, Department of Computer Science, Université Ferhat Abbas Setif 1, Setif, Algeria

    Nadjet Kamel

Authors
  1. Imane Messaoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadjet Kamel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Imane Messaoudi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Messaoudi, I., Kamel, N. A multi-objective bat algorithm for community detection on dynamic social networks . Appl Intell 49, 2119–2136 (2019). https://doi.org/10.1007/s10489-018-1386-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-018-1386-9

Keywords

Search

Navigation