A detecting community method in complex networks with fuzzy clustering

Detection of community structure in complex networks is a significant aspect in social network analysis. A novel fuzzy clustering method is proposed in this paper, by which the community structure can be divided. In contrast to previous studies, the proposed method processes similarity of connecting vertices with fuzzy relation. In our method, we globally consider the fuzzy relation between vertices and the similarity in network topology to divide vertices into communities. In addition, smaller grained communities can be detected by adjusting fuzzy parameter. In order to avoid subjectivity in the selection of cluster number, a new modularity is introduced to evaluate the effectiveness of the clustering analysis. It's proved by experiments that the method is efficient in detecting both good communities and appropriate number of clusters.

[1]  A ZadehLotfi,et al.  Toward a generalized theory of uncertainty (GTU) , 2005 .

[2]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[4]  Brian W. Kernighan,et al.  An efficient heuristic procedure for partitioning graphs , 1970, Bell Syst. Tech. J..

[5]  Huan Liu,et al.  Community Detection and Mining in Social Media , 2010, Community Detection and Mining in Social Media.

[6]  Lotfi A. Zadeh,et al.  Toward a generalized theory of uncertainty (GTU)--an outline , 2005, Inf. Sci..

[7]  Mao-Bin Hu,et al.  Detect overlapping and hierarchical community structure in networks , 2008, ArXiv.

[8]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[9]  Michalis Vazirgiannis,et al.  Clustering and Community Detection in Directed Networks: A Survey , 2013, ArXiv.

[10]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

[11]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[12]  Andrea Lancichinetti,et al.  Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.

[13]  T. Nepusz,et al.  Fuzzy communities and the concept of bridgeness in complex networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Shihua Zhang,et al.  Identification of overlapping community structure in complex networks using fuzzy c-means clustering , 2007 .

[15]  Xiaowei Xu,et al.  SCAN: a structural clustering algorithm for networks , 2007, KDD '07.

[16]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Palma Blonda,et al.  A survey of fuzzy clustering algorithms for pattern recognition. I , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Jian-Guo Liu,et al.  Detecting community structure in complex networks via node similarity , 2010 .

[20]  S. Strogatz.  Exploring complex networks , 2001, Nature.

[21]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[22]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[23]  Jia Song,et al.  Clustering Algorithms for Detecting Functional Modules in protein Interaction Networks , 2009, J. Bioinform. Comput. Biol..

[24]  Steve Gregory,et al.  Fuzzy overlapping communities in networks , 2010, ArXiv.

[25]  Dr. V. Thiagarasu,et al.  A Review on Overlapping Community Detection Algorithms , 2013 .

[26]  Boleslaw K. Szymanski,et al.  Overlapping community detection in networks: The state-of-the-art and comparative study , 2011, CSUR.

[27]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[29]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[30]  Lotfi A. Zadeh,et al.  Toward a generalized theory of uncertainty (GTU) - an outline , 2005, GrC.

[31]  Javier Béjar,et al.  Clustering algorithm for determining community structure in large networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  Shihua Zhang,et al.  Uncovering fuzzy community structure in complex networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  Padhraic Smyth,et al.  A Spectral Clustering Approach To Finding Communities in Graph , 2005, SDM.

[35]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[36]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.