Social ties, homophily and extraversion--introversion to generate complex networks

Abstract Many interconnected systems and particularly social interactions can be modeled as networks. These networks often exhibit common properties such as high clustering coefficient, low average path lengths and degree distributions following power-law. Networks having these properties are called small world-scale free networks or simply complex networks. Recent interest in complex networks has catalysed the development of algorithmic models to artificially generate these networks. Often these algorithms introduce network properties in the model regardless of their social interpretation resulting in networks which are statistically similar but structurally different from real world networks. In this paper, we focus on social networks and apply concepts of social ties, homophily and extraversion-introversion to develop a model for social networks with small world and scale free properties. We claim that the proposed model produces networks which are structurally similar to real world social networks.

[1]  N. Christakis,et al.  and Tastes, ties, and time: A new social network dataset using Facebook. , 2008 .

[2]  P. Pattison,et al.  New Specifications for Exponential Random Graph Models , 2006 .

[3]  S. N. Dorogovtsev,et al.  Exactly solvable small-world network , 1999, cond-mat/9907445.

[4]  Jari Saramäki,et al.  A comparative study of social network models: Network evolution models and nodal attribute models , 2008, Soc. Networks.

[5]  Enmin Feng,et al.  From unweighted to weighted networks with local information , 2007 .

[6]  S H Strogatz,et al.  Random graph models of social networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Erik M. Bollt,et al.  What is Special about Diffusion on Scale-Free Nets? , 2004 .

[8]  Sajid Saleem,et al.  Empirical Analysis of Seed Selection Criterion in Influence Mining for Different Classes of Networks , 2013, 2013 International Conference on Cloud and Green Computing.

[9]  Faraz Zaidi,et al.  Generating online social networks based on socio-demographic attributes , 2014, J. Complex Networks.

[10]  Tao Zhou,et al.  Local preferential attachment model for hierarchical networks , 2009 .

[11]  Rajesh Kasturirangan,et al.  Multiple Scales in Small-World Networks , 1999 .

[12]  Duncan J. Watts,et al.  Six Degrees: The Science of a Connected Age , 2003 .

[13]  Mario Cannataro,et al.  Protein-to-protein interactions: Technologies, databases, and algorithms , 2010, CSUR.

[14]  Jennifer Badham,et al.  A Spatial Approach to Network Generation for Three Properties: Degree Distribution, Clustering Coefficient and Degree Assortativity , 2010, J. Artif. Soc. Soc. Simul..

[15]  Beom Jun Kim,et al.  Growing scale-free networks with tunable clustering. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Arnaud Sallaberry,et al.  Tunable and Growing Network Generation Model with Community Structures , 2013, 2013 International Conference on Cloud and Green Computing.

[17]  Sebastian Schnettler,et al.  A structured overview of 50 years of small-world research , 2009, Soc. Networks.

[18]  Dang Yan-zhong,et al.  Multistage Random Growing Small-World Networks with Power-Law Degree Distribution , 2006 .

[19]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[20]  Garry Robins,et al.  An introduction to exponential random graph (p*) models for social networks , 2007, Soc. Networks.

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

[22]  S. N. Dorogovtsev,et al.  Evolution of networks , 2001, cond-mat/0106144.

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

[24]  Faraz Zaidi,et al.  Small world networks and clustered small world networks with random connectivity , 2012, Social Network Analysis and Mining.

[25]  Xianmin Geng,et al.  Degree correlations in citation networks model with aging , 2009 .

[26]  S. N. Dorogovtsev,et al.  Evolution of networks with aging of sites , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[27]  Andrea Lancichinetti,et al.  Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Faraz Zaidi,et al.  VAST 2008 Challenge: Social network dynamics using cell phone call patterns , 2008 .

[29]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[30]  A RAPOPORT,et al.  A study of a large sociogram. , 2007 .

[31]  G. Caldarelli,et al.  Assortative model for social networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  Han Zhu,et al.  Effect of aging on network structure. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  Jorge Finke,et al.  On the formation of structure in growing networks , 2013, ArXiv.

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

[35]  Richard M. Karp,et al.  Algorithms for graph partitioning on the planted partition model , 2001, Random Struct. Algorithms.

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

[37]  D. Krackhardt.  The strength of strong ties: The importance of Philos in organizations , 2003 .

[38]  A. Rapoport.  Contribution to the theory of random and biased nets , 1957 .

[39]  刘建国,et al.  Multistage Random Growing Small-World Networks with Power-Law Degree Distribution , 2006 .

[40]  Béla Bollobás,et al.  Mathematical results on scale‐free random graphs , 2005 .

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

[42]  V. Eguíluz,et al.  Growing scale-free networks with small-world behavior. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  Xiaofan Wang,et al.  Generalized local-world models for weighted networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[44]  Ove Frank,et al.  http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained , 2007 .

[45]  R. Guimerà,et al.  The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[47]  Gabrielle Demange,et al.  A Survey of Network Formation Models: Stability and Efficiency , 2005 .

[48]  Garry Robins,et al.  A spatial model for social networks , 2006 .

[49]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[50]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[51]  Edoardo M. Airoldi,et al.  A Survey of Statistical Network Models , 2009, Found. Trends Mach. Learn..

[52]  Faraz Zaidi,et al.  Maritime constellations: a complex network approach to shipping and ports , 2012 .

[53]  Jianwei Wang,et al.  Evolving Small-World Networks based on the Modified BA Model , 2008, 2008 International Conference on Computer Science and Information Technology.

[54]  Z. Duan,et al.  A weighted local-world evolving network model with aging nodes , 2011 .

[55]  John Scott,et al.  Social network analysis: developments, advances, and prospects , 2010, Social Network Analysis and Mining.

[56]  R. Burt.  Brokerage and Closure: An Introduction to Social Capital , 2005 .

[57]  L. R. Silva,et al.  Scale-free homophilic network , 2013 .

[58]  Satu Elisa Virtanen,et al.  PROPERTIES OF NONUNIFORM RANDOM GRAPH MODELS , 2003 .

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

[60]  Guy Melançon,et al.  Model for generating artificial social networks having community structures with small-world and scale-free properties , 2013, Social Network Analysis and Mining.

[61]  Adrian E. Raftery,et al.  Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models , 2009, Soc. Networks.

[62]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[63]  David Auber,et al.  Tulip - A Huge Graph Visualization Framework , 2004, Graph Drawing Software.

[64]  Peihua Fu,et al.  An Evolving Scale-free Network with Large Clustering Coefficient , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[65]  Jean-Loup Guillaume,et al.  Bipartite graphs as models of complex networks , 2006 .