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Community structure detection in social networks based on dictionary learning

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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.

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References

  1. Newman M E, Girvan M. Finding and evaluating community structure in networks. Phys Rev E, 2004, 69: 026113

    Article  Google Scholar 

  2. Pujol JM, Béjar J, Delgado J. Clustering algorithm for determining community structure in large networks. Phys Rev E, 2006, 74: 016107

    Article  Google Scholar 

  3. White S, Smyth P. A spectral clustering approach to finding communities in graphs. In: SIAM International Data Mining Conference. Newport Beach: SIAM, 2005. 76–84

    Google Scholar 

  4. Newman M E J. Detecting community structure in networks. Eur Phys J B, 2004, 38: 321–330

    Article  Google Scholar 

  5. Wasserman S, Faust K. Social Network Analysis: Methods and Applications, Structural Analysis in the Social Sciences. Cambridge: Cambridge University Press, 1994.

    Book  Google Scholar 

  6. Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401: 788–791

    Article  Google Scholar 

  7. Wang F, Li T, Wang X, et al. Community discovery using nonnegative matrix factorization. Data Min Knowl Disc, 2011, 22: 493–521

    Article  MATH  Google Scholar 

  8. Lee D D, Seung H S. Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems. Vancouver: MIT Press, 2001. 556–562

    Google Scholar 

  9. Mairal J, Bach F, Ponce J, et al. Online learning for matrix factorization and sparse coding. J Mach Learn Res, 2010, 11: 19–60

    MathSciNet  MATH  Google Scholar 

  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. 3501–3508

    Google Scholar 

  11. Efron, B, Hastie T, Johnstone L, et al. Least angle regression. Ann Stat, 2004, 32: 407–499

    Article  MathSciNet  MATH  Google Scholar 

  12. Ding C, Li T, Jordan M. Convex and semi-nonnegative matrix factorization. IEEE Trans Pattern Anal, 2010, 32: 45–55

    Article  Google Scholar 

  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. 232–241

    Google Scholar 

  14. Newman M E J. Modularity and community structure in networks. Nat Acad Sci USA, 2006, 103: 8577–8582

    Article  Google Scholar 

  15. Prelić A, Bleuler S, Zimmermann P, et al. A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics, 2006, 22: 1122–1129

    Article  Google Scholar 

  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. 877–886

    Chapter  Google Scholar 

  17. Zachary W W. An information flow model for conflict and fission in small groups. J Anthropol Res, 1977, 33: 452–473

    Google Scholar 

  18. Girvan M, Newman M E J. Community structure in social and biological networks. P Nat Acad Sci USA, 2002, 99: 7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  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: 396–405

    Article  Google Scholar 

  20. Knuth D E. The Stanford GraphBase: a Platform for Combinatorial Computing. MA: Addison-Wesley, 1993.

    Google Scholar 

  21. Newman M E J. Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality. Phys Rev E, 2001, 64: 016132

    Article  Google Scholar 

  22. Newman M E J. Finding community structure in networks using the eigenvectors of matrices. Phys Rev E, 2006, 74: 036104

    Article  MathSciNet  Google Scholar 

  23. Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks. Nature, 1998, 393: 440–442

    Article  Google Scholar 

  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: 1535–1550

    Article  Google Scholar 

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Correspondence to ZhongYuan Zhang.

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Zhang, Z. Community structure detection in social networks based on dictionary learning. Sci. China Inf. Sci. 56, 1–12 (2013). https://doi.org/10.1007/s11432-011-4319-3

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  • DOI: https://doi.org/10.1007/s11432-011-4319-3

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