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Multi-objective evolutionary algorithm using problem-specific genetic operators for community detection in networks

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Abstract

Automatic network clustering is an important method for mining the meaningful communities of complex networks. Uncovered communities help to understand the potential system structure and functionality. Many algorithms that use multiple optimization criteria and optimize a population of solutions are difficult to apply to real systems because they suffer a long optimization process. In this paper, in order to accelerate the optimization process and to uncover multiple significant community structures more effectively, a multi-objective evolutionary algorithm is proposed and evaluated using problem-specific genetic mutation and group crossover, and problem-specific initialization. Since crossover operators mainly contribute to performance of genetic algorithms, more problem-specific group crossover operators are introduced and evaluated for intelligent evolution of population. The experiments on both artificial and real-world networks demonstrate that the proposed evolutionary algorithm with problem-specific genetic operations has effective performance on discovering the community structure of networks.

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References

  1. Barber MJ, Clark JW (2009) Detecting network communities by propagating labels under constraints. Phys Rev E 80:02612

    Article  Google Scholar 

  2. Brandes U, Delling D, Gaertler M, Gorke R, Hoefer M, Nikoloski Z, Wagner D (2008) On modularity clustering. IEEE Trans Knowl Data Eng 20(2):172–188

    Article  Google Scholar 

  3. Chen D, Zou F, Lu R, Yu L, Li Z, Wang J (2016) Multi-objective optimization of community detection using discrete teaching-learning-based optimization with decomposition. Inf Sci 369:402–418

    Article  MathSciNet  Google Scholar 

  4. Corne DW, Jerram NR, Knowles JD, Oates MJ (2001) PESA-II: region-based selection in evolutionary multiobjective optimization. In: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann Publishers, pp 283–290

  5. Danon L, Diaz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech Theory Exp 2005(09):P0900

    Article  Google Scholar 

  6. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester

    MATH  Google Scholar 

  7. Deb K, Pratap A, Agarwal SA, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  8. Dorogovtsev SN, Mendes JFF (2003) Evolution of networks: from biological nets to the internet and WWW. Oxford University Press, Oxford

    Book  Google Scholar 

  9. Flake GW, Lawrence S, Giles CL, Coetzee FM (2002) Self-organization and identification of web communities. IEEE Comput 35(3):66–71

    Article  Google Scholar 

  10. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–114

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  13. Gleiser P, Danon L (2003) Community structure in jazz. Adv Complex Syst 06:565

    Article  Google Scholar 

  14. Gong M, Jiao L, Du H, Bo L (2008) Multiobjective immune algorithm with nondominated neighbor based selection. Evol Comput 16:225–255

    Article  Google Scholar 

  15. Gong M, Chen X, Ma L, Zhang Q, Jiao L (2013) Identification of multi-resolution network structures with multi-objective immune algorithm. Appl Soft Comput 13:1705–1717

    Article  Google Scholar 

  16. Gong M, Fu B, Jiao L, Du H (2011) A memetic algorithm for community detection in networks. Phys Rev E 84(5):056101

    Article  Google Scholar 

  17. Gong M, Ma L, Zhang Q, Jiao L (2012) Community detection in networks by using multiobjective evolutionary algorithm with decomposition. Phys A 391(15):4050–4060

    Article  Google Scholar 

  18. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78:046110

    Article  Google Scholar 

  19. Li Y, Liu J, Liu C (2014) A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks. Soft Comput 18(2):329–348

    Article  Google Scholar 

  20. Liu X, Murata T (2010) Advanced modularity-specialized label propagation algorithm for detecting communities in networks. Phys A Stat Mech Appl 389(7):1493–1500

    Article  Google Scholar 

  21. 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 54:396–405

    Article  Google Scholar 

  22. Moscato P, Cotta C (2003) A gentle introduction to memetic algorithms. Int Ser Oper Res Manag Sci 57:105–144

    MathSciNet  MATH  Google Scholar 

  23. Ma L, Gong M, Liu J, Cai Q, Jiao L (2014) Multi-level learning based memetic algorithm for community detection. Appl Soft Comput 19:121–133

    Article  Google Scholar 

  24. Mirsaleh MR, Meybodi MR (2016) A Michigan memetic algorithm for solving the community detection problem in complex network. Neurocomputing 214:535–545

    Article  Google Scholar 

  25. Neri F, Cotta C, Moscato P (eds) (2011) Handbook of memetic algorithms. SCI, vol 379. Springer, Berlin

    Google Scholar 

  26. Ong Y-S, Lim M, Chen X (2010) Memetic computation past, present, future. IEEE Comput Intell Mag 5(2):24–31

    Article  Google Scholar 

  27. Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435:814–818

    Article  Google Scholar 

  28. Park Y, Song M (1998) A genetic algorithm for clustering problems. In: Proceedings of the third annual conference on genetic programming, pp 568–575

  29. Pizzuti C (2008) Ga-net: a genetic algorithm for community detection in social networks. In: PPSN, pp 1081–1090

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

    Article  Google Scholar 

  31. Porter MA, Onnela J-P, Mucha PJ (2009) Communities in networks. Not Am Math Soc 56(1082–1097):1164–1166

    MathSciNet  MATH  Google Scholar 

  32. Pothen A, Sinmon H, Liou K-P (1990) Partitioning sparse matrices with eigenvectors of graphs. SIAM J Matrix Anal Appl l(11):430–452

    Article  MathSciNet  Google Scholar 

  33. Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying clusters in networks. Proc Natl Acad Sci USA 101(9):2658–2663

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási A-L (2002) Hierarchical organization of modularity in metabolic networks. Science 297:1551–1555

    Article  Google Scholar 

  36. Rizman Žalik K (2015) Maximal neighbor similarity reveals real communities in networks. Sci Rep 5:1837

    Google Scholar 

  37. Shang R, Bai J, Jiao L, Jin C (2013) Community detection based on modularity and an improved genetic algorithm. Phys A 392:1215–1231

    Article  Google Scholar 

  38. Schuetz P, Caflish A (2008) Efficient modularity optimization by multistep greedy algorithm and node refinement. Phys Rev E 77(4):046112

    Article  Google Scholar 

  39. Shi C, Yan ZY, Wang Y, Cai YN, Wu B (2010) A genetic algorithm for detecting communities in large-scale complex networks. Adv Complex Syst 13(1):3–17

    Article  MathSciNet  Google Scholar 

  40. Shi C, Yan Z, Cai Y, Wu B (2012) Multi-objective community detection in complex networks. Appl Soft Comput 12:850–859

    Article  Google Scholar 

  41. Strogatz SH (2001) Exploring complex networks. Nature 410:268–276

    Article  Google Scholar 

  42. Tasgin M, Herdagdelen A, Bingol H (2007) Community detection in complex networks using genetic algorithms. arXiv:0711.0491

  43. The network was compiled by V. Krebs and is unpublished, but can found on Krebs’ web site. http://www.orgnet.com/divided.html

  44. Wu F, Huberman B (2004) Finding communities in linear time: a physics approach. Eur Phys J B 38:331–338

    Article  Google Scholar 

  45. Wu P, Pan L (2015) Multi-objective community detection based on memetic algorithm. Plos ONE 10(5):e0126845

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Slovenian Research Agency (Grant Numbers: P2-0041, J2-6764). We would like to express our deepest gratitude to the anonymous reviewers for their valuable suggestions and corrections of the paper.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, Faculty of Natural Science and Mathematics, University of Maribor, Maribor, Slovenia

    Krista Rizman Žalik

  2. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Borut Žalik

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  1. Krista Rizman Žalik
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  2. Borut Žalik
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Correspondence to Krista Rizman Žalik.

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Žalik, K.R., Žalik, B. Multi-objective evolutionary algorithm using problem-specific genetic operators for community detection in networks. Neural Comput & Applic 30, 2907–2920 (2018). https://doi.org/10.1007/s00521-017-2884-0

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