Structure and Evolution of Online Social Networks

Social networks are complex systems which evolve through interactions among a growing set of actors or users. A popular methodology of studying such systems is to use tools of complex network theory to analyze the evolution of the networks, and the topological properties that emerge through the process of evolution. With the exponential rise in popularity of Online Social Networks (OSNs) in recent years, there have been a number of studies which measure the topological properties of such networks. Several network evolution models have also been proposed to explain the emergence of these properties, such as those based on preferential attachment, heterogeneity of nodes, and triadic closure. We survey some of these studies in this chapter. We also describe in detail a preferential attachment based model to analyze the evolution of OSNs in the presence of restrictions on node-degree that are presently being imposed in all popular OSNs.

[1]  Emilio Ferrara,et al.  Topological Features of Online Social Networks , 2011, ArXiv.

[2]  Markus Strohmaier,et al.  Understanding Co-evolution of Social and Content Networks on Twitter , 2012, #MSM.

[3]  Peter Druschel,et al.  Online social networks: measurement, analysis, and applications to distributed information systems , 2009 .

[4]  K. Kaski,et al.  Scale-free networks generated by random walkers , 2004, cond-mat/0404088.

[5]  Lars Backstrom,et al.  The Anatomy of the Facebook Social Graph , 2011, ArXiv.

[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]  M. Newman.  Clustering and preferential attachment in growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

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

[10]  M E J Newman,et al.  Random graphs with clustering. , 2009, Physical review letters.

[11]  H. Simon,et al.  ON A CLASS OF SKEW DISTRIBUTION FUNCTIONS , 1955 .

[12]  Cecilia Mascolo,et al.  Evolution of a location-based online social network: analysis and models , 2012, IMC '12.

[13]  Ben Y. Zhao,et al.  On the bursty evolution of online social networks , 2012, HotSocial '12.

[14]  Paul L. Krapivsky,et al.  Popularity-driven networking , 2011, ArXiv.

[15]  M Girvan,et al.  Structure of growing social networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Ginestra Bianconi,et al.  Competition and multiscaling in evolving networks , 2001 .

[17]  Seungyeop Han,et al.  Analysis of topological characteristics of huge online social networking services , 2007, WWW '07.

[18]  Haibo Hu,et al.  Disassortative mixing in online social networks , 2009, 0909.0450.

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

[20]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[21]  M. Newman,et al.  Why social networks are different from other types of networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Anthony Bonato,et al.  Models of Online Social Networks , 2009, Internet Math..

[23]  Ernesto Estrada,et al.  Spectral scaling and good expansion properties in complex networks , 2006, Europhysics Letters (EPL).

[24]  Tatsuya Akutsu,et al.  On the degree distribution of projected networks mapped from bipartite networks , 2011 .

[25]  Shankar Kumar,et al.  Video suggestion and discovery for youtube: taking random walks through the view graph , 2008, WWW.

[26]  Niloy Ganguly,et al.  Understanding how both the partitions of a bipartite network affect its one-mode projection , 2011, 1105.3902.

[27]  Harry Eugene Stanley,et al.  Preferential attachment in the interaction between dynamically generated interdependent networks , 2012, ArXiv.

[28]  P. Erdos,et al.  On the strength of connectedness of a random graph , 1964 .

[29]  Silvia Gazzola,et al.  Energetic BEM for domain decomposition in 2D wave propagation problems , 2011 .

[30]  Marco Rosa,et al.  Four degrees of separation , 2011, WebSci '12.

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

[32]  M. A. Muñoz,et al.  Scale-free networks from varying vertex intrinsic fitness. , 2002, Physical review letters.

[33]  Vittorio Loreto,et al.  Network properties of folksonomies , 2007, AI Commun..

[34]  Anthony Bonato,et al.  A geometric model for on-line social networks , 2010, MSM '10.

[35]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[36]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

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

[38]  Loet Leydesdorff,et al.  Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks , 2011, J. Informetrics.

[39]  Ravi Kumar,et al.  Structure and evolution of online social networks , 2006, KDD '06.

[40]  James P. Bagrow,et al.  Natural emergence of clusters and bursts in network evolution , 2012, Physical Review X.

[41]  S. N. Dorogovtsev,et al.  Self-organization of collaboration networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[42]  Stanley Milgram,et al.  An Experimental Study of the Small World Problem , 1969 .

[43]  Jon M. Kleinberg,et al.  The Directed Closure Process in Hybrid Social-Information Networks, with an Analysis of Link Formation on Twitter , 2010, ICWSM.

[44]  Fang Wu,et al.  Social Networks that Matter: Twitter Under the Microscope , 2008, First Monday.

[45]  Jure Leskovec,et al.  Microscopic evolution of social networks , 2008, KDD.

[46]  Emilio Ferrara,et al.  A large-scale community structure analysis in Facebook , 2011, EPJ Data Science.

[47]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[48]  D J PRICE,et al.  NETWORKS OF SCIENTIFIC PAPERS. , 1965, Science.

[49]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[50]  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.

[51]  Krishna P. Gummadi,et al.  Growth of the flickr social network , 2008, WOSN '08.

[52]  Niloy Ganguly,et al.  Effects of a soft cut-off on node-degree in the Twitter social network , 2012, Comput. Commun..

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

[54]  Giulio Cimini,et al.  Temporal effects in the growth of networks , 2011, Physical review letters.

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

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

[57]  S Redner,et al.  Degree distributions of growing networks. , 2001, Physical review letters.