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Complexity
Volume 2017, Article ID 3719428, 10 pages
https://doi.org/10.1155/2017/3719428
Research Article

Social Network Community Detection Using Agglomerative Spectral Clustering

Department of Computer Engineering, Inha University, Incheon, Republic of Korea

Correspondence should be addressed to Sanggil Kang; rk.ca.ahni@gnakgs

Received 18 April 2017; Revised 24 July 2017; Accepted 23 August 2017; Published 7 November 2017

Academic Editor: Katarzyna Musial

Copyright © 2017 Ulzii-Utas Narantsatsralt and Sanggil Kang. 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. S. Fortunato, “Community detection in graphs,” Physics Reports. A Review Section of Physics Letters, vol. 486, no. 3-5, pp. 75–174, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. J. Leskovec, K. J. Lang, and M. Mahoney, “Empirical comparison of algorithms for network community detection,” in Proceedings of the 19th International World Wide Web Conference (WWW '10), pp. 631–640, ACM, New York, NY, USA, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Girvan and M. E. 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
  4. M. E. Newman, “Community detection and graph partitioning,” EPL (Europhysics Letters), vol. 103, no. 2, Article ID 28003, 2013. View at Publisher · View at Google Scholar
  5. M. E. Newman, “Spectral methods for community detection and graph partitioning,” Physical Review E, vol. 88, no. 4, 2013. View at Publisher · View at Google Scholar
  6. S. C. Johnson, “Hierarchical clustering schemes,” Psychometrika, vol. 32, no. 3, pp. 241–254, 1967. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Zhao and G. Karypis, “Hierarchical clustering algorithms for document datasets,” Data Mining and Knowledge Discovery, vol. 10, no. 2, pp. 141–168, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. U. von Luxburg, “A tutorial on spectral clustering,” Statistics and Computing, vol. 17, no. 4, pp. 395–416, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. Y. Ng. Andrew, M. I. Jordan, and Weiss Y., “On spectral clustering analysis and an algorithm,” in Proceedings of the Advances in Neural Information Processing Systems, British Columbia, Canada, 2001.
  10. J. Yang and J. Leskovec, “Defining and evaluating network communities based on ground-truth,” in Proceedings of the 12th IEEE International Conference on Data Mining, ICDM 2012, pp. 745–754, bel, December 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. G. Karypis and V. Kumar, “A fast and high quality multilevel scheme for partitioning irregular graphs,” SIAM Journal on Scientific Computing, vol. 20, no. 1, pp. 359–392, 1998. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. B. W. Kernighan and S. Lin, “An efficient heuristic procedure for partitioning graphs,” The Bell System Technical Journal, vol. 49, no. 1, pp. 291–307, 1970. View at Publisher · View at Google Scholar
  13. H. N. Djidjev, “A scalable multilevel algorithm for graph clustering and community structure detection,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4936, pp. 117–128, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Clauset, C. Moore, and M. E. J. Newman, “Hierarchical structure and the prediction of missing links in networks,” Nature, vol. 453, no. 7191, pp. 98–101, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. M. C. Ramos, “Divisive and hierarchical clustering techniques to analyse variability of rainfall distribution patterns in a Mediterranean region,” Atmospheric Research, vol. 57, no. 2, pp. 123–138, 2001. View at Publisher · View at Google Scholar · View at Scopus
  16. L. Lin, T. Luo, J. Fu, Z. Ji, and D. Xiao, “A new community detection based on agglomeration mechanism,” in Proceedings of the IEEE 2nd International Conference on Computing, Control and Industrial Engineering, CCIE 2011, pp. 352–355, chn, August 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Leng, J. Wang, P. Wang, and X. Chen, “Hierarchical Agglomeration Community Detection Algorithm via Community Similarity Measures,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 10, no. 6, pp. 1510–1518, 2012. View at Publisher · View at Google Scholar
  18. I. S. Dhillon, Y. Guan, and B. Kulis, “Kernel k-means, spectral clustering and normalized cuts,” in Proceedings of the KDD-2004 - Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 551–556, Seattle, Wash, USA, August 2004. View at Scopus
  19. C. Alzate and J. A. K. Suykens, “Multiway spectral clustering with out-of-sample extensions through weighted kernel PCA,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 335–347, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. R. Langone, R. Mall, C. Alzate, and J. A. K. Suykens, “Kernel spectral clustering and applications,” Unsupervised Learning Algorithms, pp. 135–161, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. R. Langone, C. Alzate, and J. A. K. Suykens, “Kernel spectral clustering for community detection in complex networks,” in Proceedings of the 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012, Queensland, Australia, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Mall, R. Langone, and J. A. K. Suykens, “Agglomerative hierarchical kernel spectral data clustering,” in Proceedings of the 5th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2014, pp. 9–16, usa, December 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. B. Hendrickson and T. G. Kolda, “Graph partitioning models for parallel computing,” Parallel Computing, vol. 26, no. 12, pp. 1519–1534, 2000. View at Publisher · View at Google Scholar · View at MathSciNet
  24. C. F. Olson, “Parallel algorithms for hierarchical clustering,” Parallel Computing, vol. 21, no. 8, pp. 1313–1325, 1995. View at Publisher · View at Google Scholar · View at MathSciNet
  25. A. R. Brodtkorb, T. R. Hagen, and M. L. Sætra, “Graphics processing unit (GPU) programming strategies and trends in GPU computing,” Journal of Parallel and Distributed Computing, vol. 73, no. 1, pp. 4–13, 2013. View at Publisher · View at Google Scholar · View at Scopus