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Scalable analysis for large social networks: the data-aware mean-field approach

(2012) SOCIAL INFORMATICS. p.406-419
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Abstract
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.

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Chicago
Birkholz, Julie, Rena Bakhshi, Ravindra Harige, Maarten van Steen, and Peter Groenewegen. 2012. “Scalable Analysis for Large Social Networks: The Data-aware Mean-field Approach.” Social Informatics: 406–419.
APA
Birkholz, J., Bakhshi, R., Harige, R., van Steen, M., & Groenewegen, P. (2012). Scalable analysis for large social networks: the data-aware mean-field approach. SOCIAL INFORMATICS, 406–419.
Vancouver
1.
Birkholz J, Bakhshi R, Harige R, van Steen M, Groenewegen P. Scalable analysis for large social networks: the data-aware mean-field approach. SOCIAL INFORMATICS. 2012;406–19.
MLA
Birkholz, Julie, Rena Bakhshi, Ravindra Harige, et al. “Scalable Analysis for Large Social Networks: The Data-aware Mean-field Approach.” SOCIAL INFORMATICS (2012): 406–419. Print.
@article{5761279,
  abstract     = {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.},
  author       = {Birkholz, Julie and Bakhshi,  Rena and Harige, Ravindra and van Steen, Maarten and Groenewegen, Peter},
  issn         = {0302-9743},
  journal      = {SOCIAL INFORMATICS},
  language     = {eng},
  pages        = {406--419},
  title        = {Scalable analysis for large social networks: the data-aware mean-field approach},
  year         = {2012},
}