Greedy Modularity Graph Clustering for Community Detection of Large Co-Authorship Network

  • Authors

    • Ahmed F. Al-Mukhtar
    • Eman S. Al-Shamery
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.28058
  • Graph mining, Graph clustering, Community Detection, Social networks, Complex networks, Collaborative networks.
  • Abstract

    Social networks as a domain of complex networks that can be represented as graphs according to the patterns of connections among their elements. Social Communities are a set of nodes with denser connections inside community structures than outside. The goal of graph clustering is to divide the large graph into many clusters depending on multiple similarity criteria. In this work an improved version of the Louvain method is proposed, the Greedy Modularity Graph Clustering for Community Detection of Large Co-AuthorshipNetwork (GMGC)which introduces a new concept of weighted edges to enhance the accuracy of the Community Discovery for the large networks. The method is compared with other states of art methods mainly, Vertices Similarity First and Community Mean (VSFCM), and Generalized Louvain method for community detection in large networks (FKCD). Extensive experimental results have been madeon different datasets. The experimental results showed that the proposed method outperforms the other states of arts comparative methods according to the modularity optimization and community partitions evaluations measures.

     

     

     


  • References

    1. [1] M. Panda, S. Dehuri, and G.-N. Wang, Eds., Social Networking, vol. 65. Cham: Springer International Publishing, 2014.

      [2] D. F. Nettleton, “Data mining of social networks represented as graphs,†Comput. Sci. Rev., vol. 7, pp. 1–34, Feb. 2013.

      [3] S. Fortunato and D. Hric, “Community detection in networks: A user guide,†Phys. Rep., vol. 659, pp. 1–44, Nov. 2016.

      [4] S. Fortunato, “Community detection in graphs,†Phys. Rep., vol. 486, no. 3–5, pp. 75–174, Feb. 2010.

      [5] H. Zhou, J. Li, J. Li, F. Zhang, and Y. Cui, “A graph clustering method for community detection in complex networks,†Phys. Stat. Mech. Its Appl., vol. 469, pp. 551–562, Mar. 2017.

      [6] M. P. Boobalan, D. Lopez, and X. Z. Gao, “Graph clustering using k-Neighbourhood Attribute Structural similarity,†Appl. Soft Comput., vol. 47, pp. 216–223, Oct. 2016.

      [7] T. Yamazaki, N. Shimizu, H. Kobayashi, and S. Yamauchi, “Weighted Micro-Clustering: Application to Community Detection in Large-Scale Co-Purchasing Networks with User Attributes,†2016, pp. 131–132.

      [8] L. Bai, X. Cheng, J. Liang, and Y. Guo, “Fast graph clustering with a new description model for community detection,†Inf. Sci., vol. 388–389, pp. 37–47, May 2017.

      [9] K. Zhou, A. Martin, Q. Pan, and Z. Liu, “Median evidential c-means algorithm and its application to community detection,†Knowl.-Based Syst., vol. 74, pp. 69–88, 2015.

      [10] S. A. Moosavi, M. Jalali, N. Misaghian, S. Shamshirband, and M. H. Anisi, “Community detection in social networks using user frequent pattern mining,†Knowl. Inf. Syst., vol. 51, no. 1, pp. 159–186, Apr. 2017.

      [11] V. Greeshma and K. S. Vani, “Community Detection in Networks Using Page Rank Vectors,†Int. J. Bioinforma. Biosci., vol. 5, no. 1/2/3/4, pp. 01–07, Dec. 2015.

      [12] P. Ambika and M. B. Rajan, “Survey on diverse facets and research issues in social media mining,†in Research Advances in Integrated Navigation Systems (RAINS), International Conference on, 2016, pp. 1–6.

      [13] M. E. J. Newman and M. Girvan, “Finding and evaluating community structure in networks,†Phys. Rev. E, vol. 69, no. 2, Feb. 2004.

      [14] A. Clauset, M. E. Newman, and C. Moore, “Finding community structure in very large networks,†Phys. Rev. E, vol. 70, no. 6, p. 066111, 2004.

      [15] M. Chen, K. Kuzmin, and B. K. Szymanski, “Community detection via maximization of modularity and its variants,†IEEE Trans. Comput. Soc. Syst., vol. 1, no. 1, pp. 46–65, 2014.

      [16] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,†J. Stat. Mech. Theory Exp., vol. 2008, no. 10, p. P10008, Oct. 2008.

      [17] X. Chen, J.-F. Guo, F.-C. Liu, and C.-Y. Zhang, “Study on similarity based on connection degree in social network,†Clust. Comput., vol. 20, no. 1, pp. 167–178, Mar. 2017.

      [18] P. De Meo, E. Ferrara, G. Fiumara, and A. Provetti, “Generalized louvain method for community detection in large networks,†in Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on, 2011, pp. 88–93.

      [19] M. Ley, “DBLP: some lessons learned,†Proc. VLDB Endow., vol. 2, no. 2, pp. 1493–1500, 2009.

      [20] W. W. Zachary, “An Information Flow Model for Conflict and Fission in Small Groups,†J. Anthropol. Res., vol. 33, no. 4, pp. 452–473, 1977.

      [21] D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, “The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations,†Behav. Ecol. Sociobiol., vol. 54, no. 4, pp. 396–405, Sep. 2003.

      [22] R. Guimera, L. Danon, A. Diaz-Guilera, F. Giralt, and A. Arenas, “Self-similar community structure in organisations,†Phys. Rev. E, vol. 68, no. 6, Dec. 2003.

      [23] P. Gleiser and L. Danon, “Community Structure in Jazz,†Adv. Complex Syst., vol. 06, no. 04, pp. 565–573, Dec. 2003.

      [24] M. Girvan and M. E. J. Newman, “Community structure in social and biological networks,†Proc. Natl. Acad. Sci., vol. 99, no. 12, pp. 7821–7826, Jun. 2002.

      [25] J. Leskovec and C. Faloutsos, “Sampling from large graphs,†in Pro. 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006, pp. 631–636.

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  • How to Cite

    F. Al-Mukhtar, A., & S. Al-Shamery, E. (2018). Greedy Modularity Graph Clustering for Community Detection of Large Co-Authorship Network. International Journal of Engineering & Technology, 7(4.19), 857-863. https://doi.org/10.14419/ijet.v7i4.19.28058

    Received date: 2019-03-01

    Accepted date: 2019-03-01

    Published date: 2018-11-27