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Research Article

Multi-facet Community Detection from Bipartite Networks

Xu Yongcheng, Chen Ling and Zou Shengrong
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Detecting communities from networks is one of the important and challenging research topics in social network analysis, especially from bipartite network. In unipartite network, communities are usually represented as sets of nodes within which connections are dense but between which connections are sparse. However, communities in unipartite networks are not suitable to bipartite network, because there is only one-to-one correspondence between communities of different types. In this study we propose an algorithm for detecting communities from bipartite network based on ant colony optimization. Present algorithm allows many-to-many correspondence between communities in different parts. Experimental results demonstrate that tour algorithm can extract multi-facet communities from bipartite networks and obtain high quality of community partitioning.

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  How to cite this article:

Xu Yongcheng, Chen Ling and Zou Shengrong, 2013. Multi-facet Community Detection from Bipartite Networks. Journal of Applied Sciences, 13: 3137-3144.

DOI: 10.3923/jas.2013.3137.3144


1:  Barabasi, A.L. and Z.N. Oltvai, 2004. Network biology: Understanding the cell's functional organization. Nat. Rev. Genet., 5: 101-113.
CrossRef  |  PubMed  |  Direct Link  |  

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

3:  Broder, A., R. Kumar, F. Maghoul, R. Stata and A. Tomkins et al., 2000. Graph structure in the web. Comput. Networks, 33: 309-320.
CrossRef  |  Direct Link  |  

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

5:  Dorigo, M., V. Maniezzo and A. Colorni, 1996. Ant system: Optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B: Cybern., 26: 29-41.
CrossRef  |  Direct Link  |  

6:  Girvan, M. and M.E.J. Newman, 2002. Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA., 99: 7821-7826.
CrossRef  |  Direct Link  |  

7:  Guimera, R., M. Sales-Pardo and L.A. Amaral, 2007. Module identification in bipartite and directed network. Phys. Rev. E, Vol. 76. 10.1103/PhysRevE.76.036102

8:  Lehmann, S., M. Schwartz and L.K. Hansen, 2008. Biclique communities. Phys. Rev. E, Vol. 78. 10.1103/PhysRevE.78.016108

9:  Liu, X. and T. Murata, 2009. Community detection in large-scale bipartite networks. Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, September 15-18, 2009, Milan, Italy, pp: 50-57.

10:  Liu, X. and T. Murata, 2009. How does label propagation algorithm work in bipartite networks? Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Volume 3, September 15-18, 2009, Washington, DC., USA., pp: 5-8.

11:  Long, B., X. Wu, Z. Zhang and P.S. Yu, 2007. Community learning by graph approximation. Proceedings of the 7th IEEE International Conference on Data Mining, October 28-31, 2007, Omaha, NE., pp: 232-241.

12:  Newman, M.E.J., 2006. Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA., 103: 8577-8582.
CrossRef  |  Direct Link  |  

13:  Porter, M.A., J.P. Onnela and P.J. Mucha, 2009. Communities in networks. Notices Am. Math. Soc., 56: 1082-1097.
Direct Link  |  

14:  Radicchi, F., C. Castellano, F. Cecconi, V. Loreto and D. Parisi, 2004. Defining and identifying communities in networks. Proc. Natl. Acad. Sci. USA., 101: 2658-2663.
CrossRef  |  

15:  Raghavan, U.N., R. Albert and S. Kumara, 2007. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E, Vol. 76. 10.1103/PhysRevE.76.036106

16:  Scott, J. and M. Hughes, 1980. The Anatomy of Scottish Capital: Scottish Companies and Scottish Capital, 1900-1979. Croom Helm, London, ISBN: 9780773505285, Pages: 291.

17:  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.

18:  Watts, D.J. and S.H. Strogatz, 1998. Collective dynamics of small-world networks. Nature, 393: 440-442.
CrossRef  |  PubMed  |  Direct Link  |  

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