Uncovering Overlap Community Structure in Complex Networks Using Particle Competition

Identification and classification of overlap nodes in communities is an important topic in data mining. In this paper, a new clustering method to uncover overlap nodes in complex networks is proposed. It is based on particles walking and competing with each other, using random-deterministic movement. The new community detection algorithm can output not only hard labels, but also continuous-valued output (soft labels), which corresponds to the levels of membership from the nodes to each of the communities. Computer simulations were performed with synthetic and real data and good results were achieved.

[1]  Liang Zhao,et al.  Particle competition for complex network community detection. , 2008, Chaos.

[2]  Ian H. Witten,et al.  Data Mining: Practical Machine Learning Tools and Techniques, 3/E , 2014 .

[3]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .

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

[5]  Sholom M. Weiss,et al.  Predictive data mining - a practical guide , 1997 .

[6]  Ian Witten,et al.  Data Mining , 2000 .

[7]  Sergey N. Dorogovtsev,et al.  Evolution of Networks: From Biological Nets to the Internet and WWW (Physics) , 2003 .

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

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

[10]  Stefan Bornholdt,et al.  Detecting fuzzy community structures in complex networks with a Potts model. , 2004, Physical review letters.

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

[12]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.

[13]  Simon Parsons,et al.  Principles of Data Mining by David J. Hand, Heikki Mannila and Padhraic Smyth, MIT Press, 546 pp., £34.50, ISBN 0-262-08290-X , 2004, The Knowledge Engineering Review.

[14]  Albert-László Barabási,et al.  Evolution of Networks: From Biological Nets to the Internet and WWW , 2004 .

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

[16]  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.

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

[18]  R. Carter.  11 – IT and society , 1991 .

[19]  Stefan Bornholdt,et al.  Handbook of Graphs and Networks: From the Genome to the Internet , 2003 .

[20]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[21]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

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