Community structure detection in social networks based on dictionary learning

Science China Information Sciences

Volumn 56, Issue 7, 2013, Pages 1-12

  Zhang, ZhongYuan ^a  

^a CENTRAL UNIVERSITY OF FINANCE AND ECONOMICS   (China)

Author keywords

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

Indexed keywords

COMMUNITY STRUCTURE DETECTIONS; COMMUNITY STRUCTURES; DICTIONARY LEARNING; DICTIONARY LEARNING ALGORITHMS; LEAST SQUARES ERRORS; NONNEGATIVE MATRIX FACTORIZATION; REAL-WORLD NETWORKS; SYNTHETIC DATASETS;

LEARNING ALGORITHMS;

SOCIAL SCIENCES;

EID: 84879982194     PISSN: 1674733X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11432-011-4319-3     Document Type: Article

References (24)

1. Newman M E, Girvan M. Finding and evaluating community structure in networks. Phys Rev E, 2004, 69: 026113.
2. Pujol JM, Béjar J, Delgado J. Clustering algorithm for determining community structure in large networks. Phys Rev E, 2006, 74: 016107.
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.
4. Newman M E J. Detecting community structure in networks. Eur Phys J B, 2004, 38: 321-330.
5. Wasserman S, Faust K. Social Network Analysis: Methods and Applications, Structural Analysis in the Social Sciences. Cambridge: Cambridge University Press, 1994.
6. Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401: 788-791.
7. Wang F, Li T, Wang X, et al. Community discovery using nonnegative matrix factorization. Data Min Knowl Disc, 2011, 22: 493-521.
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.
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.
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.
11. Efron, B, Hastie T, Johnstone L, et al. Least angle regression. Ann Stat, 2004, 32: 407-499.
12. Ding C, Li T, Jordan M. Convex and semi-nonnegative matrix factorization. IEEE Trans Pattern Anal, 2010, 32: 45-55.
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.
14. Newman M E J. Modularity and community structure in networks. Nat Acad Sci USA, 2006, 103: 8577-8582.
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.
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.
17. Zachary W W. An information flow model for conflict and fission in small groups. J Anthropol Res, 1977, 33: 452-473.
18. Girvan M, Newman M E J. Community structure in social and biological networks. P Nat Acad Sci USA, 2002, 99: 7821-7826.
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.
20. Knuth D E. The Stanford GraphBase: a Platform for Combinatorial Computing. MA: Addison-Wesley, 1993.
21. Newman M E J. Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality. Phys Rev E, 2001, 64: 016132.
22. Newman M E J. Finding community structure in networks using the eigenvectors of matrices. Phys Rev E, 2006, 74: 036104.
23. Watts D J, Strogatz S H. Collective dynamics of 'small-world' networks. Nature, 1998, 393: 440-442.
24. 3D. Nature Protocol, 2009, 4: 1535-1550.