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The Scientific World Journal
Volume 2014, Article ID 402345, 22 pages
http://dx.doi.org/10.1155/2014/402345
Research Article

Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an 710071, China

Received 21 October 2013; Accepted 22 December 2013; Published 2 March 2014

Academic Editors: Z. Cui and X. Yang

Copyright © 2014 Jingjing Ma 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. 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 Scopus
  2. M. E. J. Newman and M. Girvan, “Finding and evaluating community structure in networks,” Physical Review E, vol. 69, Article ID 026113, 2004. View at Google Scholar
  3. B. Yang, W. K. Cheung, and J. Liu, “Community mining from signed social networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 10, pp. 1333–1348, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. G. Palla, A.-L. Barabási, and T. Vicsek, “Quantifying social group evolution,” Nature, vol. 446, no. 7136, pp. 664–667, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Greene, D. Doyle, and P. Cunningham, “Tracking the evolution of communities in dynamic social networks,” in Proceedings of the International Conference on Advances in Social Network Analysis and Mining (ASONAM '10), pp. 176–183, Odense, Denmark, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Yang, Y. Chi, S. Zhu, Y. Gong, and R. Jin, “Detecting communities and their evolutions in dynamic social networks—a Bayesian approach,” Machine Learning, vol. 82, no. 2, pp. 157–189, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. Y.-R. Lin, Y. Chi, S. Zhu, H. Sundaram, and B. L. Tseng, “Analyzing communities and their evolutions in dynamic social networks,” ACM Transactions on Knowledge Discovery from Data, vol. 3, no. 2, article 8, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. D. Chakrabarti, R. Kumar, and A. Tomkins, “Evolutionary clustering,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '06), pp. 554–560, Philadelphia, Pa, USA, August 2006. View at Scopus
  9. Q. Zhang and H. Li, “MOEA/D: a multiobjective evolutionary algorithm based on decomposition,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712–731, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Danon, A. Daz-Guilera, J. Duch, and A. Arenas, “Comparing community structure identification,” Journal of Statistical Mechanics, vol. 2005, Article ID P09008, 2005. View at Publisher · View at Google Scholar
  11. A. Konstantinidis and K. Yang, “Multi-objective energy-efficient dense deployment in Wireless Sensor Networks using a hybrid problem-specific MOEA/D,” Applied Soft Computing Journal, vol. 11, no. 6, pp. 4117–4134, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Li and Q. Zhang, “Multiobjective optimization problems with complicated pareto sets, MOEA/ D and NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 284–302, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. Q. Zhang, W. Liu, E. Tsang, and B. Virginas, “Expensive multiobjective optimization by MOEA/D with gaussian process model,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 3, pp. 456–474, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. F. Folino and C. Pizzuti, “A multiobjective and evolutionary clustering method for dynamic networks,” in Proceedings of the International Conference on Advances in Social Network Analysis and Mining (ASONAM '10), pp. 256–263, Odense, Denmark, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. U. N. Raghavan, R. Albert, and S. Kumara, “Near linear time algorithm to detectcommunity structures in large-scale networks,” Physical Review E, vol. 76, no. 3, Article ID 036106, 11 pages, 2007. View at Google Scholar
  17. E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: improving the strength Pareto evolutionary algorithm for multiobjective optimization,” in Proceedings of the Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pp. 95–100, 2001.
  18. C. A. Coello Coello, G. T. Pulido, and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256–279, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Gong, Q. Cai, X. Chen, and L. Ma, “Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 1, pp. 82–97, 2014. View at Publisher · View at Google Scholar
  20. M. Gong, X. Chen, L. Ma, Q. Zhang, and L. Jiao, “Identification of multi-resolution network structures with multi-objective immune algorithm,” Applied Soft Computing, vol. 13, no. 4, pp. 1705–1717, 2013. View at Google Scholar
  21. L. Ma, M. Gong, Q. Cai, and L. Jiao, “Enhancing community integrity of networks against multilevel targeted attacks,” Physical Review E, vol. 88, no. 2, Article ID 022810, 2013. View at Google Scholar
  22. M. Gong, L. Jiao, H. Du, and L. Bo, “Multiobjective immune algorithm with nondominated neighbor-based selection,” Evolutionary Computation, vol. 16, no. 2, pp. 225–255, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Gong, L. Ma, Q. Zhang, and L. Jiao, “Community detection in networks by using multiobjective evolutionary algorithm with decomposition,” Physica A, vol. 391, no. 15, pp. 4050–4060, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Gong, L. Zhang, L. Ma, and L. Jiao, “Community detection in dynamic social networks based on multiobjective immune algorithm,” Journal of Computer Science and Technology, vol. 27, no. 3, pp. 455–467, 2012. View at Google Scholar
  25. S. Fortunato, “Community detection in graphs,” Physics Reports, vol. 486, no. 3–5, pp. 75–174, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. J. Hopcroft, O. Khan, B. Kulis, and B. Selman, “Tracking evolving communities in large linked networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 1, pp. 5249–5253, 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. Y. Chi, X. Song, D. Zhou, K. Hino, and B. L. Tseng, “Evolutionary spectral clustering by incorporating temporal smoothness,” in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '07), pp. 153–162, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. L. Tang, H. Liu, J. Zhang, and Z. Nazeri, “Community evolution in dynamic multi-mode networks,” in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '08), pp. 677–685, August 2008. View at Publisher · View at Google Scholar · View at Scopus
  29. Y.-R. Lin, Y. Chi, S. Zhu, H. Sundaram, and B. L. Tseng, “FacetNet: a framework for analyzing communities and their evolutions in dynamic networks,” in Proceedings of the 17th International Conference on World Wide Web (WWW '08), pp. 685–694, Beijing, China, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. M. S. Kim and J. Han, “A particle-and-density based evolutionary clustering method for dynamic networks,” Proceedings of the VLDB Endowment, vol. 2, no. 1, pp. 622–633, 2009. View at Google Scholar
  31. A. Lancichinetti and S. Fortunato, “Consensus clustering in complex networks,” Scientific Reports, vol. 2, article 336, 2012. View at Publisher · View at Google Scholar
  32. R. Ahmed and G. Karypis, “Algorithms for mining the evolution of conserved relational states in dynamic networks,” Knowledge and Information Systems, vol. 33, no. 3, pp. 603–630, 2012. View at Google Scholar
  33. J. Kunegis, D. Fay, and C. Bauckhage, “Spectral evolution in dynamic networks,” Knowledge and Information Systems, vol. 37, no. 1, pp. 1–36, 2013. View at Publisher · View at Google Scholar
  34. D. Patnaik, S. Laxman, and N. Ramakrishnan, “Discovering excitatory relationships using dynamic Bayesian networks,” Knowledge and Information Systems, vol. 29, no. 2, pp. 273–303, 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. W. Peng and T. Li, “Temporal relation co-clustering on directional social network and author-topic evolution,” Knowledge and Information Systems, vol. 26, no. 3, pp. 467–486, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. L. Zhao, X. Guan, and R. Yuan, “Modeling collective blogging dynamics of popular incidental topics,” Knowledge and Information Systems, vol. 31, no. 2, pp. 371–387, 2012. View at Publisher · View at Google Scholar · View at Scopus
  37. K. Miettinen, Nonlinear Multiobjective Optimization, Kluwer, Norwell, Mass, USA, 1999.
  38. M. G. Gong, B. Fu, L. C. Jiao, and H. F. Du, “A memetic algorithm for community detection in networks,” Physical Review E, vol. 84, no. 5, Article ID 056101, 2011. View at Google Scholar
  39. Y. J. Park and M. S. Song, “A genetic algorithm for clustering problems,” in Proceedings of the 3rd Annual Conference on Genetic Programming, pp. 568–575, 1998.
  40. J. Handl and J. Knowles, “An evolutionary approach to multiobjective clustering,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 1, pp. 56–76, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. C. Pizzuti, “GA-Net: a genetic algorithm for community detection in social networks,” in Proceedings of the International Conference on Parallel Problem Solving from Nature (PPSN '08), pp. 1081–1090, 2008.
  42. M. J. Barber and J. W. Clark, “Detecting network communities by propagating labels under constraints,” Physical Review E, vol. 80, no. 2, Article ID 026129, 11 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
  43. X. Liu and T. Murata, “Advanced modularity-specialized label propagation algorithm for detecting communities in networks,” Physica A: Statistical Mechanics and its Applications, vol. 389, no. 7, pp. 1493–1500, 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. Z. Li, S. Zhang, R. S. Wang, X. S. Zhang, and L. Chen, “Quantitative function for community detection,” Physical Review E, vol. 77, no. 3, Article ID 036109, 9 pages, 2008. View at Google Scholar
  45. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” Journal of Statistical Mechanics, vol. 2008, no. 10, Article ID P10008, 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. 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
  47. J. Chen and Y. Saad, “Dense subgraph extraction with application to community detection,” IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 7, pp. 1216–1230, 2010. View at Publisher · View at Google Scholar
  48. R. McGill, J. Tukey, and W. Larsen, “Variations of Boxplots,” The American Statistician, vol. 32, pp. 12–16, 1978. View at Google Scholar
  49. 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
  50. W. D. Nooy, A. Mrvar, and V. Batagelj, Exploratory Social Network Analysis with Pajek, Cambridge University Press, New York, NY, USA, 2005.
  51. Q. Ye, T. Zhu, D. Hu, B. Wu, N. Du, and B. Wang, “Cell phone mini challenge award: social network accuracy—exploring temporal communication in mobile call graphs,” in Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST '08), pp. 207–208, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  52. S. Q. Yang, B. Wu, and B. Wang, “Tracking the evolution in social network: methods and results,” in Complex Sciences, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pp. 693–706, 2009. View at Google Scholar
  53. S. Asur, S. Parthasarathy, and D. Ucar, “An event-based framework for characterizing the evolutionary behavior of interaction graphs,” ACM Transactions on Knowledge Discovery from Data, vol. 3, no. 4, article 16, 2009. View at Publisher · View at Google Scholar · View at Scopus