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.
Similar content being viewed by others
References
M.E.J. Newman, M. Girvan, Phys. Rev. E 69, 026113 (2004)
M.E.J. Newman, Proc. Natl. Acad. Sci. 103, 8577 (2006)
A. Clauset, M.E.J. Newman, C. Moore, Phys. Rev. E 70, 066111 (2004)
M.E.J. Newman, Phys. Rev. E 69, 066133 (2004)
V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, J. Stat. Mech. Theor. Exp. 10, P10008 (2008)
J.Q. Jiang, L.J. McQuay, Physica A 391, 854 (2012)
F. Wu, B. Huberman, Eur. Phys. J. B 38, 331 (2004)
M.E.J. Newman, Phys. Rev. E 74, 036104 (2006)
J. Duch, A. Arenas, Phys. Rev. E 72, 027104 (2005)
P. Schumm, C. Scoglio, Journal of Computational Science 3, 356 (2012)
S. Muff, F. Rao, A. Caflisch, Phys. Rev. E 72, 056107 (2005)
Y. Pana, D.-H. Li, J.-G. Liu, J.-Z. Liang, Physica A 389, 284 (2010)
S. Fortunato, Phys. Rep. 486, 75 (2010)
P. Pons, M. Latapy, J. Graph Algorithms Appl. 10, 191 (2006)
K. Steinhaeuser, N.V. Chawla, Pat. Recog. Lett. 31, 413 (2010)
D. Lai, C. Nardini, H. Lu, Phys. Rev. E 83, 016115 (2011)
D. Chen, M. Shang, Z. Lv, Y. Fu, Physica A 389 4177 (2010)
W. Wang, C. Li, in Proceedings of the International Conference on Computational Aspects of Social Networks, Taiyuan, 2010, pp. 607–610
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
T. Falkowski, A. Barth, M. Spiliopoulou, in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Silicon Valley, 2007, pp. 112–115
X. Xu, N. Yuruk, Z. Feng, T. Schweiger, Knowledge discovery and data mining, 13th edn. (ACM, 2007) pp. 824–833
J. Huang, H. Sunb, J. Han, B. Feng, Physica A 390, 2160 (2011)
A. Pothen, H. Simon, K.P. Liou, SIAM J. Matrix Anal. Appl. 11, 430 (1990)
A. Capocci et al., Physica A 352, 669 (2005)
T. Zhou, L.Y. Lu, Y.C. Zhang, Eur. Phys. J. B 71, 623 (2009)
H. Zhou, Phys. Rev. E 67, 061901 (2003)
H. Zhou, R. Lipowsky, Lect. Notes Comput. Sci. 3038, 1062 (2004)
M. Rosvall, C.T. Bergstrom, Proc. Natl. Acad. Sci. U.S.A. 104, 7327 (2007)
M. Rosvall, C.T. Bergstrom, Proc. Natl. Acad. Sci. U.S.A. 105, 1118 (2008)
H. Li, Y. Wang, L. Wu, J. Zhang, X. Zhang, Phys. Rev. E 86, 016109 (2012)
Z. Li, S. Zhang, R.-S. Wang, X.-S. Zhang, L. Chen, Phys. Rev. E 77, 036109 (2008)
H. Lu, H. Wei, Physica A. 391, 6156 (2012)
K. Li, X. Gong, S. Guan, C.-H. Lai, Physica A 391, 1788 (2012)
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)
A. Fred, A. Jain, in Proceedings of the International Conference on Pattern Recognition, Quebec City, 2002, pp. 276–280
A. Fred, A. Jain, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Madison, 2003, p. 128
D. Lusseau, K. Schneider, O.J. Boisseau, P. Haase, E. Slooten, S.M. Dawson, Behav. Ecol. Sociobiol. 54, 396 (2003)
A. Lancichinetti, S. Fortunato, Phys. Rev. E 80, 056117 (2009)
S. Van Dongen, Ph.D. thesis, University of Utrecht, 2000
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Published:
DOI: https://doi.org/10.1140/epjb/e2012-30697-5