Scalable Analysis for Large Social Networks: The Data-Aware Mean-Field Approach

Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a mean-field model which allows for the construction of a population-specific model informed from empirical research for predicting links from both network and social properties in large social networks.. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters.

[1]  A. Barabasi,et al.  Evolution of the social network of scientific collaborations , 2001, cond-mat/0104162.

[2]  Kara L Hall,et al.  The ecology of team science: understanding contextual influences on transdisciplinary collaboration. , 2008, American journal of preventive medicine.

[3]  Hans Akkermans,et al.  Web dynamics as a random walk: how and why power laws occur , 2012, WebSci '12.

[4]  D. Goldberg,et al.  Assessing experimentally derived interactions in a small world , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Akbar Zaheer,et al.  The Genesis and Dynamics of Organizational Networks , 2012, Organ. Sci..

[6]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[7]  Marko A. Rodriguez,et al.  On the relationship between the structural and socioacademic communities of a coauthorship network , 2008, J. Informetrics.

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

[9]  Patrick Doreian,et al.  Introduction to the special issue on network dynamics , 2010, Soc. Networks.

[10]  Peter Mika,et al.  Application of semantic technology for social network analysis in the sciences , 2006, Scientometrics.

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

[12]  Tom A. B. Snijders,et al.  Exponential Random Graph Models for Social Networks , 2013 .

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

[14]  Lyle H. Ungar,et al.  Statistical Relational Learning for Link Prediction , 2003 .

[15]  Roger Guimerà,et al.  Team Assembly Mechanisms Determine Collaboration Network Structure and Team Performance , 2005, Science.

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

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

[18]  Boudewijn R. Haverkort,et al.  Mean-field framework for performance evaluation of push-pull gossip protocols , 2011, Perform. Evaluation.

[19]  Paul J. Campbell,et al.  Patterns of Collaboration in Mathematical Research (Book) , 2003 .

[20]  Valdis E. Krebs,et al.  Mapping Networks of Terrorist Cells , 2001 .

[21]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[22]  Benjamin F. Jones,et al.  Multi-University Research Teams: Shifting Impact, Geography, and Stratification in Science , 2008, Science.

[23]  Donald de B. Beaver,et al.  Studies in scientific collaboration , 2005, Scientometrics.

[24]  Donald de B. Beaver,et al.  Studies in scientific collaboration Part III. Professionalization and the natural history of modern scientific co-authorship , 1979, Scientometrics.

[25]  Elizabeth A. Corley,et al.  Scientists' collaboration strategies: implications for scientific and technical human capital , 2004 .

[26]  K. Bauman,et al.  The contribution of influence and selection to adolescent peer group homogeneity: the case of adolescent cigarette smoking. , 1994, Journal of personality and social psychology.

[27]  R. L. Stratonovich.  CONDITIONAL MARKOV PROCESSES , 1960 .

[28]  M. Greene.  The demise of the lone author , 2007, Nature.

[29]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[30]  Boudewijn R. Haverkort,et al.  Automating the Mean-Field Method for Large Dynamic Gossip Networks , 2010, QEST.

[31]  Katy Börner,et al.  A Multi-Level Systems Perspective for the Science of Team Science , 2010, Science Translational Medicine.

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

[33]  Gourab Ghoshal,et al.  Exact solutions for models of evolving networks with addition and deletion of nodes. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  David M. Pennock,et al.  Statistical relational learning for document mining , 2003, Third IEEE International Conference on Data Mining.

[35]  R. Bakhshi.  Gossiping Models : Formal Analysis of Epidemic Protocols , 2011 .

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

[37]  Ben Taskar,et al.  Link Prediction in Relational Data , 2003, NIPS.

[38]  M. Newman.  Coauthorship networks and patterns of scientific collaboration , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[39]  Tom A. B. Snijders,et al.  Introduction to stochastic actor-based models for network dynamics , 2010, Soc. Networks.