Advertisement

Science China Information Sciences

, Volume 56, Issue 7, pp 1–12 | Cite as

Community structure detection in social networks based on dictionary learning

  • ZhongYuan Zhang
Research Papers

Abstract

Discovering community structures is a fundamental problem concerning how to understand the topology and the functions of complex network. In this paper, we propose how to apply dictionary learning algorithm to community structure detection. We present a new dictionary learning algorithm and systematically compare it with other state-of-the-art models/algorithms. The results show that the proposed algorithm is highly effectively at finding the community structures in both synthetic datasets, including three types of data structures, and real world networks coming from different areas.

Keywords

community structure detection dictionary learning least squares error regression nonnegative matrix factorization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Newman M E, Girvan M. Finding and evaluating community structure in networks. Phys Rev E, 2004, 69: 026113CrossRefGoogle Scholar
  2. 2.
    Pujol JM, Béjar J, Delgado J. Clustering algorithm for determining community structure in large networks. Phys Rev E, 2006, 74: 016107CrossRefGoogle Scholar
  3. 3.
    White S, Smyth P. A spectral clustering approach to finding communities in graphs. In: SIAM International Data Mining Conference. Newport Beach: SIAM, 2005. 76–84Google Scholar
  4. 4.
    Newman M E J. Detecting community structure in networks. Eur Phys J B, 2004, 38: 321–330CrossRefGoogle Scholar
  5. 5.
    Wasserman S, Faust K. Social Network Analysis: Methods and Applications, Structural Analysis in the Social Sciences. Cambridge: Cambridge University Press, 1994.CrossRefGoogle Scholar
  6. 6.
    Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401: 788–791CrossRefGoogle Scholar
  7. 7.
    Wang F, Li T, Wang X, et al. Community discovery using nonnegative matrix factorization. Data Min Knowl Disc, 2011, 22: 493–521zbMATHCrossRefGoogle Scholar
  8. 8.
    Lee D D, Seung H S. Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems. Vancouver: MIT Press, 2001. 556–562Google Scholar
  9. 9.
    Mairal J, Bach F, Ponce J, et al. Online learning for matrix factorization and sparse coding. J Mach Learn Res, 2010, 11: 19–60MathSciNetzbMATHGoogle Scholar
  10. 10.
    Ramírez I, Sprechmann P, Sapiro G. Classification and clustering via dictionary learning with structured incoherence and shared features. In: CVPR. San Francisco: IEEE, 2010. 3501–3508Google Scholar
  11. 11.
    Efron, B, Hastie T, Johnstone L, et al. Least angle regression. Ann Stat, 2004, 32: 407–499MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Ding C, Li T, Jordan M. Convex and semi-nonnegative matrix factorization. IEEE Trans Pattern Anal, 2010, 32: 45–55CrossRefGoogle Scholar
  13. 13.
    Long B, Xu X, Zhang Z, et al. Community learning by graph approximation. In: ICDM’ 07: Proceedings of the 2007 Seventh IEEE International Conference on Data Mining. Omaha: IEEE, 2007. 232–241Google Scholar
  14. 14.
    Newman M E J. Modularity and community structure in networks. Nat Acad Sci USA, 2006, 103: 8577–8582CrossRefGoogle Scholar
  15. 15.
    Prelić A, Bleuler S, Zimmermann P, et al. A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics, 2006, 22: 1122–1129CrossRefGoogle Scholar
  16. 16.
    Wu J, Xiong H, Chen J. Adapting the right measures for K-means clustering. In: KDD’ 09: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris: ACM, 2009. 877–886CrossRefGoogle Scholar
  17. 17.
    Zachary W W. An information flow model for conflict and fission in small groups. J Anthropol Res, 1977, 33: 452–473Google Scholar
  18. 18.
    Girvan M, Newman M E J. Community structure in social and biological networks. P Nat Acad Sci USA, 2002, 99: 7821–7826MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Lusseau D, Schneider K, Boisseau O J, et al. The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav Ecol Sociobiol, 2003, 54: 396–405CrossRefGoogle Scholar
  20. 20.
    Knuth D E. The Stanford GraphBase: a Platform for Combinatorial Computing. MA: Addison-Wesley, 1993.Google Scholar
  21. 21.
    Newman M E J. Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality. Phys Rev E, 2001, 64: 016132CrossRefGoogle Scholar
  22. 22.
    Newman M E J. Finding community structure in networks using the eigenvectors of matrices. Phys Rev E, 2006, 74: 036104MathSciNetCrossRefGoogle Scholar
  23. 23.
    Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks. Nature, 1998, 393: 440–442CrossRefGoogle Scholar
  24. 24.
    Theocharidis A, Dongen S van, Enright A J, et al. Network visualization and analysis of gene expression data using BioLayout Express3D. Nature Protocol, 2009, 4: 1535–1550CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.School of StatisticsCentral University of Finance and EconomicsBeijingChina

Personalised recommendations