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access icon openaccess Efficient community detection method based on attribution of nodes in complex network

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References

    1. 1)
      • 1. Watts, D.J.: ‘A twenty-first century science’, Nature, 2007, 445, (7127), p. 489.
    2. 2)
      • 2. Wang, F.Y., Zeng, D., Carley, K.M., et al: ‘Social computing: from social informatics to social intelligence’, IEEE Intell. Syst., 2007, 22, (2), pp. 7983.
    3. 3)
      • 3. Dorogovtsev, S.N., Mendes, J.F.F.: ‘Evolution of networks from biological nets to the internet and WWW’ (Oxford University Press, Budapest, Hungary, 2003).
    4. 4)
      • 4. Newman, M.E.J.: ‘The structure and function of complex networks’, SIAM Rev., 2003, 45, pp. 167256.
    5. 5)
      • 5. Albert, R., Barabási, A.L.: ‘Statistical mechanisms of complex networks’, Rev. Mod. Phys., 2002, 74, pp. 4797.
    6. 6)
      • 6. Wang, Y.W., Wang, H.O., Xiao, J.W., et al: ‘Synchronization of complex dynamical networks under recoverable attacks’, Automatica, 2010, 46, (1), pp. 197203.
    7. 7)
      • 7. Fortunato, S.: ‘Community detection in graphs’, Phys. Rep., 2010, 486, (3–5), pp. 75174.
    8. 8)
      • 8. Leskovec, J., Lang, K.J., Mahoney, M.: ‘Empirical comparison of algorithms for network community detection’. Proc. of the 19th Int. Conf. on World Wide Web, New York, 2010, pp. 631640.
    9. 9)
      • 9. Newman, M.E.: ‘Spectral methods for community detection and graph partitioning’, Phys. Rev. E, 2013, 88, p. 042822.
    10. 10)
      • 10. Newman, M.E., Girvan, M: ‘Finding and evaluating community structure in networks’, Phys. Rev. E, 2004, 69, (2), p. 026113.
    11. 11)
      • 11. Newman, M.E.: ‘Fast algorithm for detecting community structure in networks’, Phys. Rev. E, 2004, 69, (6), p. 066133.
    12. 12)
      • 12. Friedman, J., Hastie, T., Tibshirani, R.: ‘The elements of statistical learning’, Vol. 1 in: Springer Series in Statistics (Springer, Berlin, 2001).
    13. 13)
      • 13. Shang, R., Bai, J., Jiao, L., et al: ‘Community detection based on modularity and an improved genetic algorithm’, Physica A, 2013, 392, pp. 12151231.
    14. 14)
      • 14. Tasgin, M., Herdagdelen, A., Bingol, H.: ‘Community detection in complex networks using genetic algorithms’, 2006, arXiv:0711.0491.
    15. 15)
      • 15. Duch, J., Arenas, A.: ‘Community detection in complex networks using external optimization’, Phys. Rev. E, 2005, 72, p. 027104.
    16. 16)
      • 16. Girvan, M., Newman, M.E.J.: ‘Community structure in social and biological networks’, Proc. Natl. Acad. Sci., 2002, 99, (12), pp. 78217826.
    17. 17)
      • 17. Jiang, Y., Jia, C., Yu, J.: ‘An efficient community detection method based on rank centrality’, Phys. A, 2013, 392, (9), pp. 21822194.
    18. 18)
      • 18. Li, Y., Jia, C.: ‘A parameter-free community detection method based on centrality and dispersion of nodes in complex networks’, Physica A, 2015, 438, pp. 321334.
    19. 19)
      • 19. Wang, T., Wang, H.: ‘A novel cosine distance for detecting communities in complex networks’, Phys. A, 2015, 437, pp. 2135.
    20. 20)
      • 20. Žalik, K.R., Žalik, B.: ‘A framework for detecting communities of unbalanced sizes in networks’, Phys. A, Stat. Mech. Appl., 2018, 490, pp. 2437.
    21. 21)
      • 21. Zhao, S., Zhao, P., Cui, Y.: ‘A network centrality measure framework for analyzing urban traffic flow: a case study of wuhan, China’, Phys. A, Stat. Mech. Appl., 2017, 478, pp. 143157.
    22. 22)
      • 22. Chen, S., Wang, Z.Z., Bao, M.H., et al: ‘Adaptive multi-resolution modularity for detecting communities in networks’, Phys. A, Stat. Mech. Appl., 2017.
    23. 23)
      • 23. Qi, X., Song, H., Wu, J., et al: ‘Eb&D: A new clustering approach for signed social networks based on both edge-betweenness centrality and density of subgraphs’, Phys. A, Stat. Mech. Appl., 2017, 482.
    24. 24)
      • 24. Bilal, S., Abdelouahab, M.: ‘Evolutionary algorithm and modularity for detecting communities in networks’, Phys. A, Stat. Mech. Appl., 2017, 473.
    25. 25)
      • 25. Ma, X., Wang, B., Yu, L.: ‘Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods’, Phys. A, Stat. Mech. Appl., 2018, 490, pp. 786802.
    26. 26)
      • 26. Newman, M.E.J., Reinert, G.: ‘Estimating the number of communities in a network’, Phys. Rev. Lett., 2016, 117, p. 078301.
    27. 27)
      • 27. Riolo, M.A., Cantwell, G.T., Reinert, G., et al: ‘Efficient method for estimating the number of communities in a network’, Phys. Rev. E, 2017, 96, p. 032310.
    28. 28)
      • 28. Chen, Q.: ‘Detecting local community structures in complex networks based on local degree central nodes’, Physica A, 2013, 392, pp. 529537.
    29. 29)
      • 29. Clauset, A.: ‘Finding local community structure in networks’, Phys. Rev. E, 2005, 72, (2).
    30. 30)
      • 30. Lusseau, D., Schneider, K., Boisseau, O.J., et al: ‘The Bottlenose Dolphin community of doubtful sound features a large proportion of long-lasting associations’, Behav. Ecol. Sociobiol., 2003, 54, (4), pp. 396405.
    31. 31)
      • 31. Zachary, W.W.: ‘An information flow model for conflict and fission in small groups’, J. Anthropol. Res., 1977, 33, pp. 452473.
    32. 32)
      • 32. Newman, M.E.J.: ‘Modularity and community structure in networks’, Proc. Natl. Acad. Sci. of the United States of America, 2006, 103, (23), pp. 85778582.
    33. 33)
      • 33. Heckerling, P.S., Gerber, B.S., Tape, T.G., et al: ‘Use of genetic algorithms for neural networks to predict community-acquired pneumonia’, Physica A, 2004, 30, pp. 7184.
    34. 34)
      • 34. Gong, M.G., Fu, B., Jiao, L.C., et al: ‘Memetic algorithm for community detection in networks’, Phys. Rev. E, 2011, 84, p. 006100.
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