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Journal of Applied Sciences

Year: 2013 | Volume: 13 | Issue: 13 | Page No.: 2421-2428
DOI: 10.3923/jas.2013.2421.2428
Community Detecting in Bipartite Network Based on Principal Components Analysis
Wei Liu and Ling Chen

Abstract: The identification of communities is significant for the understanding of network structures and functions. In this study, we propose a framework to address the problem of community detection in bipartite networks based on principal components analysis. We apply the algorithm to real-world network data, showing that the algorithm successfully finds meaningful community structures of bipartite networks.

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How to cite this article
Wei Liu and Ling Chen, 2013. Community Detecting in Bipartite Network Based on Principal Components Analysis. Journal of Applied Sciences, 13: 2421-2428.

Keywords: Bipartite network, community detecting and principal components analysis

REFERENCES
  • Costa, L.F., F.A. Rodrigues, G. Travieso and P.R.V. Boas, 2007. Characterization of complex networks: A survey of measurements. Adv. Phys., 56: 167-242.
    Direct Link    

  • Fortunato, S., 2010. Community detection in graphs. Phys. Rep., 486: 75-174.
    CrossRef    

  • Newman, M.E.J., 2004. Fast algorithm for detecting community structure in networks. Phys. Rev. E, Vol. 69.
    CrossRef    

  • Clauset, A., M.E.J. Newman and C. Moore, 2004. Finding community structure in very large networks Phys. Rev. E, 70: 066111-066116.
    CrossRef    

  • Kernighan, B.W. and S. Lin, 1970. An efficient heuristic procedure for partitioning graphs. Bell Labs Tech. J., 49: 291-307.
    CrossRef    Direct Link    

  • Xiang, B., E.H. Chen and T. Zhou, 2009. Finding community structure based on subgraph similarity. Stud. Comput. Intell., 207: 73-81.
    CrossRef    

  • Ruan, J. and W. Zhang, 2008. Identifying network communities with a high resolution. Phy. Rev., Vol. 77,

  • Duch, J. and A. Arenas, 2005. Community detection in complex networks using extremal optimization. Phys. Rev. E, Vol. 72.

  • Wang, X.T., G.R. Chen and H.T. Lu, 2007. A very fast algorithm for detecting community structures in complex networks. Phys. A, 384: 667-674.
    CrossRef    

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

  • Chen, D.B., Y. Fu and M.S. Shang, 2009. A fast and efficient heuristic algorithm for detecting community structures in complex networks. Phys. A, 388: 2741-2749.
    CrossRef    

  • Chen, W.Q., J.A. Lu and J. Liang, 2009. Research in disease-gene network based on bipartite network projection. Complex Syst. Comp. Sci., 6: 13-19.
    Direct Link    

  • Davis, A., B.B. Gardner and M.R. Gardner, 1941. Deep South. University of Chicago Press, USA

  • Barber, M.J., 2007. Modularity and community detection in bipartite networks. Phys. Rev. E, 76: 1-9.
    CrossRef    Direct Link    

  • Guimera, R., M. Sales-Pardo and L.A. Amaral, 2007. Module identification in bipartite and directed network. Phys. Rev. E, Vol. 76.
    CrossRef    

  • Murata, T. and T. Ikeya, 2010. A new modularity for detecting one-to-many correspondence of communities in bipartite networks. Adv. Complex Syst., 13: 19-31.
    Direct Link    

  • Suzuki, K. and K. Wakita, 2009. Extracting multi-facet community structure from bipartite networks. Proceedings of the International Conference on Computational Science and Engineering, Volume 4, August 29-31, 2009, Vancouver, BC., pp: 312-319.

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