Statistical approach for community mining in social networks

The popularity of social networking on the Web and the explosive combination with data mining techniques open up vast and so far unexplored opportunities for social intelligence on the Web. A network community is a special sub-network that contains a group of nodes sharing similar linked patterns. Many community mining algorithms have been developed in the past. In this work, we have presented a new algorithm BFC (breadth first clustering) which uses statistical approach for community mining in social networks. The algorithm proceeds in breadth first way and incrementally extract communities from the network. This algorithm is simple, fast and can be scaled easily for large social networks. The effectiveness of this approach has been validated using network examples.

[1]  Eytan Adar,et al.  GUESS: a language and interface for graph exploration , 2006, CHI.

[2]  Hawoong Jeong,et al.  Systematic analysis of group identification in stock markets. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Andreas Noack,et al.  An Energy Model for Visual Graph Clustering , 2003, GD.

[4]  Jiming Liu,et al.  Community Mining from Signed Social Networks , 2007, IEEE Transactions on Knowledge and Data Engineering.

[5]  Eric D. Kelsic.  Understanding complex networks with community-nding algorithms , 2005 .

[6]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

[7]  A. Arenas,et al.  Community detection in complex networks using extremal optimization. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[9]  Bernardo A. Huberman,et al.  E-Mail as Spectroscopy: Automated Discovery of Community Structure within Organizations , 2005, Inf. Soc..

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

[11]  Bernardo A. Huberman,et al.  Email as spectroscopy: automated discovery of community structure within organizations , 2003 .

[12]  Ying Zhou,et al.  Discovering Web Communities in the Blogspace , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

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

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

[15]  Wei Zhang,et al.  Detect community structure from the Enron Email Corpus Based on Link Mining , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

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

[17]  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.

[18]  Jiawei Han,et al.  Mining hidden community in heterogeneous social networks , 2005, LinkKDD '05.

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

[20]  Philip S. Yu,et al.  Focused community discovery , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[21]  C. Lee Giles,et al.  Efficient identification of Web communities , 2000, KDD '00.

[22]  Ryutaro Ichise,et al.  A mining method of communities keeping tacit knowledge , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[23]  Jiming Liu,et al.  An Autonomy Oriented Computing (AOC) Approach to Distributed Network Community Mining , 2007, First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007).

[24]  Robert E. Tarjan,et al.  Graph Clustering and Minimum Cut Trees , 2004, Internet Math..

[25]  Mohsen Jamali,et al.  Different Aspects of Social Network Analysis , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[26]  Dayou Liu,et al.  Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network , 2006, Journal of Computer Science and Technology.