Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016, Article ID 1873504, 11 pages
http://dx.doi.org/10.1155/2016/1873504
Research Article

Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks

1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
2State Key Laboratory of NBC Protection for Civilian, Research Institute of Chemical Defense, Beijing 102205, China
3Zhengzhou Coal Industry (Group) Limited Liability Company, Zhengzhou 450000, China

Received 24 September 2015; Revised 16 December 2015; Accepted 20 December 2015

Academic Editor: Juan A. Almendral

Copyright © 2016 Fanrong Meng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Coscia, F. Giannotti, and D. Pedreschi, “A classification for community discovery methods in complex networks,” Statistical Analysis and Data Mining, vol. 4, no. 5, pp. 512–546, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. M. Girvan and M. E. J. Newman, “Community structure in social and biological networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 12, pp. 7821–7826, 2002. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. W. Lin, F. Lu, and Z. Ding, “Parallel computing hierachical community approach based on weighted-graph,” Journal of Software, vol. 23, no. 6, pp. 1517–1530, 2012. View at Google Scholar
  4. G. Palla, I. Derényi, I. Farkas, and T. Vicsek, “Uncovering the overlapping community structure of complex networks in nature and society,” Nature, vol. 435, no. 7043, pp. 814–818, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. Y.-Y. Ahn, J. P. Bagrow, and S. Lehmann, “Link communities reveal multiscale complexity in networks,” Nature, vol. 466, no. 7307, pp. 761–764, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Zhu, F. Meng, and Y. Zhou, “Density based link clustering algorithm for overlapping community detection,” Journal of Computer Research and Development, vol. 50, no. 12, pp. 2520–2530, 2013. View at Google Scholar · View at Scopus
  7. T. S. Evans and R. Lambiotte, “Line graphs, link partitions, and overlapping communities,” Physical Review E, vol. 80, no. 1, Article ID 016105, pp. 145–148, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale, and D. Pedreschi, “Multidimensional networks: foundations of structural analysis,” World Wide Web, vol. 16, no. 5, pp. 567–593, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Lancichinetti, S. Fortunato, and J. Kertész, “Detecting the overlapping and hierarchical community structure in complex networks,” New Journal of Physics, vol. 11, no. 3, Article ID 033015, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. N. P. Nguyen, T. N. Dinh, D. T. Nguyen et al., “Overlapping community structures and their detection on social networks,” in Proceedings of the IEEE 3rd International Conference on Privacy, Security, Risk and Trust (PASSAT '11), and IEEE 3rd International Conference on Social Computing (SocialCom '11), pp. 35–40, IEEE, Boston, Mass, USA, October 2011.
  11. A. Lancichinetti, F. Radicchi, J. J. Ramasco, and S. Fortunato, “Finding statistically significant communities in networks,” PLoS ONE, vol. 6, no. 4, Article ID e18961, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. M. E. J. Newman and M. Girvan, “Finding and evaluating community structure in networks,” Physical Review E, vol. 69, no. 2, Article ID 026113, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Clauset, M. E. J. Newman, and C. Moore, “Finding community structure in very large networks,” Physical Review E, vol. 70, no. 6, Article ID 066111, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. Z. Ye, S. Hu, and J. Yu, “Adaptive clustering algorithm for community detection in complex networks,” Physical Review E, vol. 78, no. 4, Article ID 046115, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. U. N. Raghavan, R. Albert, and S. Kumara, “Near linear time algorithm to detect community structures in large-scale networks,” Physical Review E, vol. 76, no. 3, Article ID 036106, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Xie and B. K. Szymanski, “LabelRank: a stabilized label propagation algorithm for community detection in networks,” in Proceedings of the IEEE 2nd Network Science Workshop (NSW '13), pp. 138–143, IEEE, West Point, NY, USA, May 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Rosvall and C. T. Bergstrom, “Maps of random walks on complex networks reveal community structure,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 4, pp. 1118–1123, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Xie, B. K. Szymanski, and X. Liu, “SLPA: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process,” in Proceedings of the 11th IEEE International Conference on Data Mining Workshops (ICDMW '11), pp. 344–349, IEEE, Vancouver, Canada, December 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Xie, M. Chen, and B. K. Szymanski, “LabelRankT: incremental community detection in dynamic networks via label propagation,” in Proceedings of the Workshop on Dynamic Networks Management and Mining (DyNetMM '13), pp. 25–32, ACM, New York, NY, USA, June 2013. View at Publisher · View at Google Scholar
  20. N. P. Nguyen, T. N. Dinh, Y. Xuan, and M. T. Thai, “Adaptive algorithms for detecting community structure in dynamic social networks,” in Proceedings of the IEEE Conference on Computer Communications (INFOCOM '11), pp. 2282–2290, Shanghai, China, April 2011. View at Publisher · View at Google Scholar
  21. T. Falkowski, A. Barth, and M. Spiliopoulou, “Dengraph: a density-based community detection algorithm,” in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pp. 112–115, IEEE, November 2007. View at Scopus
  22. N. P. Nguyen, T. N. Dinh, S. Tokala, and M. T. Thai, “Overlapping communities in dynamic networks: their detection and mobile applications,” in Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, pp. 85–95, ACM, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Yang and J. Leskovec, “Defining and evaluating network communities based on ground-truth,” in Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics (MDS '12), article 3, Beijing, China, August 2012.
  24. I. S. Dhillon, Y. Guan, and B. Kulis, “Kernel k-means: spectral clustering and normalized cuts,” in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 551–556, ACM, Blacksburg, Va, USA, August 2004.