An Evolutionary Approach for Detecting Communities in Social Networks

Rapid development and wide usage of social networking applications have enabled large amounts of valuable data which can be analyzed for various reasons by companies, governments, non-profit organizations such as UN. This paper presents an evolutionary approach for detecting communities in social networks. We formulated a genetic algorithm that does not require the number of communities as input and is able to detect communities effectively in a very fast way. The performance of the proposed method is compared to its counterparts in order to show that good results can be generated. Additionally, we have done experiments using Newman's Spectral Clustering Method as a pre-processing step and it gave much better results.

[1]  Jure Leskovec,et al.  Learning to Discover Social Circles in Ego Networks , 2012, NIPS.

[2]  Tom A. B. Snijders,et al.  Social Network Analysis , 2011, International Encyclopedia of Statistical Science.

[3]  Petter Holme,et al.  Subnetwork hierarchies of biochemical pathways , 2002, Bioinform..

[4]  M. Newman,et al.  Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  P. Arabie,et al.  An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling , 1975 .

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

[7]  Diana Crane,et al.  Invisible colleges. Diffusion of knowledge in scientific communities , 1972, Medical History.

[8]  A Díaz-Guilera,et al.  Self-similar community structure in a network of human interactions. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  M E Newman,et al.  Scientific collaboration networks. I. Network construction and fundamental results. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  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.

[11]  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.

[12]  John Scott.  Social Network Analysis , 1988 .

[13]  Alessandro Vespignani,et al.  Dynamical Processes on Complex Networks , 2008 .

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

[15]  Shilpa Chakravartula,et al.  Complex Networks: Structure and Dynamics , 2014 .

[16]  David Bawden,et al.  Book Review: Evolution and Structure of the Internet: A Statistical Physics Approach. , 2006 .

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

[18]  P. Mucha,et al.  Spectral tripartitioning of networks. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  S. Boorman,et al.  Social Structure from Multiple Networks. I. Blockmodels of Roles and Positions , 1976, American Journal of Sociology.

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

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

[23]  Houssem Assadi,et al.  Detecting overlapping communities in linear time with P&A algorithm , 2005 .

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

[25]  Pablo M. Gleiser,et al.  Community Structure in Jazz , 2003, Adv. Complex Syst..

[26]  Emanuel Falkenauer,et al.  Genetic Algorithms and Grouping Problems , 1998 .

[27]  John Scott.  What is social network analysis , 2010 .

[28]  Haluk Bingol,et al.  Community Detection in Complex Networks Using Genetic Algorithms , 2006, 0711.0491.

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

[30]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

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

[32]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[33]  Konstantin Avrachenkov,et al.  Cooperative Game Theory Approaches for Network Partitioning , 2017, COCOON.

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

[35]  A. Arenas,et al.  Models of social networks based on social distance attachment. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[37]  Jean Tague-Sutcliffe,et al.  An Introduction to Informetrics , 1992, Inf. Process. Manag..

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

[39]  M. Fiedler.  Algebraic connectivity of graphs , 1973 .

[40]  Alex Pothen,et al.  PARTITIONING SPARSE MATRICES WITH EIGENVECTORS OF GRAPHS* , 1990 .

[41]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

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

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