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EAMCD: an efficient algorithm based on minimum coupling distance for community identification in complex networks

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Abstract

Community structure is an important feature in many real-world networks, which can help us understand structure and function in complex networks better. In recent years, there have been many algorithms proposed to detect community structure in complex networks. In this paper, we try to detect potential community beams whose link strengths are greater than surrounding links and propose the minimum coupling distance (MCD) between community beams. Based on MCD, we put forward an optimization heuristic algorithm (EAMCD) for modularity density function to welded these community beams into community frames which are seen as a core part of community. Using the principle of random walk, we regard the remaining nodes into the community frame to form a community. At last, we merge several small community frame fragments using local greedy strategy for the modularity density general function. Real-world and synthetic networks are used to demonstrate the effectiveness of our algorithm in detecting communities in complex networks.

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References

  1. M.E.J. Newman, M. Girvan, Phys. Rev. E 69, 026113 (2004)

    Article  ADS  Google Scholar 

  2. M.E.J. Newman, Proc. Natl. Acad. Sci. 103, 8577 (2006)

    Article  ADS  Google Scholar 

  3. A. Clauset, M.E.J. Newman, C. Moore, Phys. Rev. E 70, 066111 (2004)

    Article  ADS  Google Scholar 

  4. M.E.J. Newman, Phys. Rev. E 69, 066133 (2004)

    Article  ADS  Google Scholar 

  5. V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, J. Stat. Mech. Theor. Exp. 10, P10008 (2008)

    Article  Google Scholar 

  6. J.Q. Jiang, L.J. McQuay, Physica A 391, 854 (2012)

    Article  ADS  Google Scholar 

  7. F. Wu, B. Huberman, Eur. Phys. J. B 38, 331 (2004)

    Article  ADS  Google Scholar 

  8. M.E.J. Newman, Phys. Rev. E 74, 036104 (2006)

    Article  MathSciNet  ADS  Google Scholar 

  9. J. Duch, A. Arenas, Phys. Rev. E 72, 027104 (2005)

    Article  ADS  Google Scholar 

  10. P. Schumm, C. Scoglio, Journal of Computational Science 3, 356 (2012)

    Article  Google Scholar 

  11. S. Muff, F. Rao, A. Caflisch, Phys. Rev. E 72, 056107 (2005)

    Article  ADS  Google Scholar 

  12. Y. Pana, D.-H. Li, J.-G. Liu, J.-Z. Liang, Physica A 389, 284 (2010)

    Google Scholar 

  13. S. Fortunato, Phys. Rep. 486, 75 (2010)

    Article  MathSciNet  ADS  Google Scholar 

  14. P. Pons, M. Latapy, J. Graph Algorithms Appl. 10, 191 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. K. Steinhaeuser, N.V. Chawla, Pat. Recog. Lett. 31, 413 (2010)

    Article  Google Scholar 

  16. D. Lai, C. Nardini, H. Lu, Phys. Rev. E 83, 016115 (2011)

    Article  MathSciNet  ADS  Google Scholar 

  17. D. Chen, M. Shang, Z. Lv, Y. Fu, Physica A 389 4177 (2010)

    Article  ADS  Google Scholar 

  18. W. Wang, C. Li, in Proceedings of the International Conference on Computational Aspects of Social Networks, Taiyuan, 2010, pp. 607–610

  19. J. Jin, L. Pan, C. Wang, J. Xie, in Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence, Boca Raton, 2011, pp. 513–518

  20. T. Falkowski, A. Barth, M. Spiliopoulou, in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Silicon Valley, 2007, pp. 112–115

  21. X. Xu, N. Yuruk, Z. Feng, T. Schweiger, Knowledge discovery and data mining, 13th edn. (ACM, 2007) pp. 824–833

  22. J. Huang, H. Sunb, J. Han, B. Feng, Physica A 390, 2160 (2011)

    Article  ADS  Google Scholar 

  23. A. Pothen, H. Simon, K.P. Liou, SIAM J. Matrix Anal. Appl. 11, 430 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  24. A. Capocci et al., Physica A 352, 669 (2005)

    Article  ADS  Google Scholar 

  25. T. Zhou, L.Y. Lu, Y.C. Zhang, Eur. Phys. J. B 71, 623 (2009)

    Article  ADS  MATH  Google Scholar 

  26. H. Zhou, Phys. Rev. E 67, 061901 (2003)

    Article  ADS  Google Scholar 

  27. H. Zhou, R. Lipowsky, Lect. Notes Comput. Sci. 3038, 1062 (2004)

    Article  Google Scholar 

  28. M. Rosvall, C.T. Bergstrom, Proc. Natl. Acad. Sci. U.S.A. 104, 7327 (2007)

    Article  ADS  Google Scholar 

  29. M. Rosvall, C.T. Bergstrom, Proc. Natl. Acad. Sci. U.S.A. 105, 1118 (2008)

    Article  ADS  Google Scholar 

  30. H. Li, Y. Wang, L. Wu, J. Zhang, X. Zhang, Phys. Rev. E 86, 016109 (2012)

    Article  ADS  Google Scholar 

  31. Z. Li, S. Zhang, R.-S. Wang, X.-S. Zhang, L. Chen, Phys. Rev. E 77, 036109 (2008)

    Article  ADS  Google Scholar 

  32. H. Lu, H. Wei, Physica A. 391, 6156 (2012)

    Article  ADS  Google Scholar 

  33. K. Li, X. Gong, S. Guan, C.-H. Lai, Physica A 391, 1788 (2012)

    Article  ADS  Google Scholar 

  34. J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, M. Argollo de Menezes, K. Kaski, A.-L. Barabási, J. Kertész, New J. Phys. 9, 179 (2007)

    Article  ADS  Google Scholar 

  35. A. Fred, A. Jain, in Proceedings of the International Conference on Pattern Recognition, Quebec City, 2002, pp. 276–280

  36. A. Fred, A. Jain, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Madison, 2003, p. 128

  37. D. Lusseau, K. Schneider, O.J. Boisseau, P. Haase, E. Slooten, S.M. Dawson, Behav. Ecol. Sociobiol. 54, 396 (2003)

    Article  Google Scholar 

  38. A. Lancichinetti, S. Fortunato, Phys. Rev. E 80, 056117 (2009)

    Article  ADS  Google Scholar 

  39. S. Van Dongen, Ph.D. thesis, University of Utrecht, 2000

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Authors and Affiliations

  1. School of Electronics and Information, Tongji University, Shanghai, 201804, PR China

    GuoDong Zhao, Yan Wu, YuanFang Ren & Ming Zhu

  2. School of Mathematics and Physics, Shanghai Dian Ji University, Shanghai, 201306, PR China

    GuoDong Zhao

Authors
  1. GuoDong Zhao
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  2. Yan Wu
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  3. YuanFang Ren
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  4. Ming Zhu
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Corresponding author

Correspondence to Yan Wu.

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Zhao, G., Wu, Y., Ren, Y. et al. EAMCD: an efficient algorithm based on minimum coupling distance for community identification in complex networks. Eur. Phys. J. B 86, 14 (2013). https://doi.org/10.1140/epjb/e2012-30697-5

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  • DOI: https://doi.org/10.1140/epjb/e2012-30697-5

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