Abstract
In order to make more accurate partition community structure of complex networks, this paper puts forward a new community partition algorithm. The basic idea of the algorithm depends on node similarity, and it deletes the link whose similarity is the smallest every time, then takes modularity Q as the judging standard. Computing the corresponding modularity when network occurs into pieces, and the module structure is the ultimate community structure when Q reaches its peak. This algorithm not only improves the accuracy of the original algorithms, but also makes sure that the community structure has a better quantification. When the new algorithm is applied to the complex networks, we finally find that the algorithm is effective and feasible.
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References
Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1988)
Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 7821–7826 (2002)
Kernighan, B.W., Lin, S.: A efficient heuristic procedure for partitioning graphs. Bell System Technical Journal 49, 291–307 (1970)
Fiedler, M.: Algebraic connectivity of graphs. Czech. Math. J. 23, 298–305 (1973)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. EÂ 69, 026113 (2004)
Radicchi, F., Castellano, C., Cecconi, F., et al.: Defining and identifying communities in networks. Proceedings of the National Academy of Sciences 101(9), 2658–2663 (2004)
Ting, L., Baoqing, H.: Based on clustering analysis in complex network community detection. Journal of Complex Systems and Complexity Science 4(l), 29–35 (2007) (in Chinese)
Yawen, J., Caiyan, J., Jian, Y.: The network community detection algorithm based on node similarity research. Journal of Computer Science 38(7) (2011) (in Chinese)
Newman, M.E.J.: Phys. Rev. E 69, 066133 (2004)
Xutao, W., Guanrong, C., Hongtao, L.: A very fast algorithm for detecting community structures in complex networks. Physica A384, 667–674 (2007) (in Chinese)
Newman, M.E.J.: Scientific collaboration networks. Phys. Rev. E 64, 016132 (2001)
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Du, P., Ma, Y., Wang, X. (2014). An Improved SGN Algorithm Research for Detecting Community Structure in Complex Network. In: Zu, Q., Vargas-Vera, M., Hu, B. (eds) Pervasive Computing and the Networked World. ICPCA/SWS 2013. Lecture Notes in Computer Science, vol 8351. Springer, Cham. https://doi.org/10.1007/978-3-319-09265-2_6
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DOI: https://doi.org/10.1007/978-3-319-09265-2_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09264-5
Online ISBN: 978-3-319-09265-2
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