Semi-supervised clustering algorithm for community structure detection in complex networks

Physica A: Statistical Mechanics and its Applications

Volumn 389, Issue 1, 2010, Pages 187-197

  Ma, Xiaoke ^a   Gao, Lin ^a   Yong, Xuerong ^b   Fu, Lidong ^a  

^a XIDIAN UNIVERSITY   (China)

^b UNIVERSITY OF PUERTO RICO   (Puerto Rico)

Author keywords

Community structure; Complex networks; Nonnegative matrix factorization; Semi supervised clustering

Indexed keywords

COMPLEX NETWORKS; FACTORIZATION; FUNCTIONS; MATRIX ALGEBRA; SEMI-SUPERVISED LEARNING;

COMMUNITY STRUCTURE DETECTIONS; COMMUNITY STRUCTURES; MODULARITY DENSITIES; NONNEGATIVE MATRIX FACTORIZATION; REAL-WORLD NETWORKS; SEMI-SUPERVISED CLUSTERING; SEMI-SUPERVISED CLUSTERING ALGORITHMS; SPECTRAL CLUSTERING;

CLUSTERING ALGORITHMS;

EID: 70349438985     PISSN: 03784371     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.physa.2009.09.018     Document Type: Article

References (36)

1. Girvan M., and Newmann M.E.J. Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99 12 (2002) 7821-7826
2. D. Gibson, J. Kleinberg, P. Raghavan, Inferring web communities from link topology, in: ACM ICHH, 1998, pp. 225-234
3. Lu H.C., Zhu X.P., Liu H.F., et al. The interactome as a tree-an attempt to visualize the protein-protein interaction networks in yeast. Nucleic Acids Res. 32 (2004) 4804-4811
4. Pujol J.M., Béjar J., and Delgado J. Clustering algorithms for determining community in large networks. Phys. Rev. E 74 (2006) 016107
5. S. White, P. Smyth, A spectral clustering approach to finding communities in graphs, in: SIAM ICDM, 2005
6. Wang R.S., Zhang S.H., Wang Y., et al. Clustering complex networks and biological networks by nonnegative matrix factorization with various similarity measures. Neurocomputing 72 (2008) 134-141
7. Zhang S.H., Wang R.S., and Zhang X.S. Uncovering fuzzy community structure in complex networks. Phys. Rev. E 76 (2007) 046103
8. Zhang S.H., Wang R.S., and Zhang X.S. Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A 374 (2007) 483-490
9. Newmann M.E.J. Detecting community structure in networks. Eur. Phys. J. B 38 (2004) 321-330
10. Danon L., Duch J., Diaz-Guilera A., and Arenas A. Comparing community structure identification. J. Stat. Mech. Theory Exp. (2005) P09008
11. Lee D.D., and Seung H.S. Learning the parts of objects by non-negative matrix factorization. Nature 401 (1999) 788-791
12. D.D. Lee, H.S. Seung, Algorithms for non-negative matrix factorization, in: ACNIPS, 2001, pp. 362-371
13. Newman M.E.J., and Girvan M. Finding and evaluating community structure in networks. Phys. Rev. E 69 (2004) 026113
14. Duch J., and Arenas A. Community identification using extremal optimization. Phys. Rev. E 72 (2005) 027104
15. Newman M.E.J. Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103 23 (2006) 8577-8582
16. Brandes U., Delling D., Gaertler M., et al. On modularity clustering. IEEE Trans. Knowl. Data Eng. 20 (2008) 172-188
17. Fortunato S., and Barthélemy M. Resolution limit in community detection. Proc. Natl. Acad. Sci. USA 104 36 (2007) 36-41
18. Li Z.P., Zhang S.H., Wang R.S., et al. Quantative function for community detection. Phys. Rev. E 77 (2008) 036109
19. Palla G., Derényi I., Farkas I., et al. Uncovering the overlapping community structure of complex networks in nature and society. Nature 435 (2005) 814-818
20. Ravasz E., Somera A.L., Mongru D.A., et al. Hierarchical organization of modularity in metabolic networks. Science 297 (2002) 1551-1555
21. Reichardt J., and Bornholdt S. Detecting fuzzy community structures in complex networks with a Potts model. Phys. Rev. Lett. 93 (2004) 218701
22. R.I. Kondor, J. Lafferty, Diffusion kernels on graphs and other discrete input space, in: ICML, 2002, pp. 315-322
23. Gustafsson M., H0̈rnquista M., and Lombardi A. Comparison and validation of community structures in complex networks. Physica A 367 (2006) 559-576
24. Samanta M.P., and Liang S. Predicting protein functions from redundancies in large-scale protein interaction networks. Proc. Natl. Acad. Sci. USA 100 22 (2003) 12579-12583
25. Sun S., Zhao Y., Jiao Y., et al. Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm. FEBS Lett. 580 (2006) 1891-1896
26. Wang G., Shen Y., and Luan E.J. A measure of centrality based on modularity matrix. Prog. Nat. Sci. 18 (2008) 1043-1047
27. Cristianini N., and Shawe-Taylor J. Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods (2000), Cambridge Univ. Press
28. C. Ding, X. He, H.D. Simon, On the equivalence of nonnegative matrix factorization and spectral clustering, in: SIAM ICDM, 2005, pp. 606-610
29. Rosvall M., and Bergstron C.T. An information-theoretic framework for resolving community structure in complex networks. Proc. Natl. Acad. Sci. USA 104 18 (2007) 7327-7331
30. Zachary W.W. An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33 (1977) 452-473
31. Lancichinetti A., Fourtunato S., and Radicchi F. Bechmark graphs for testing community detection algorithm. Phys. Rev. E 78 (2008) 046110
32. J.W. Berry, B. Hecdrickson, R.A. LaViolette, C.A. Phillips, Tolerating the community detection resolution limit with edge weighting, 2009. arXiv:0903.1072
33. Danon L., Diaz-Guilera A., Duch J., and Arenas A. Comparing community structure identification. J. Stat. Mech. Theory Exp. (2005) P09008
34. Newman M.E.J. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74 (2006) 036104
35. Nepusz T., Petróczi A., Négyessy L., et al. Fuzzy communities and the concept of bridgeness in complex networks. Phys. Rev. E 77 (2008) 016107
36. Medus A., Acunã G., and Dorso C.O. Detection of community structures in networks via global optimization. Physica A 358 (2005) 593-604