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

  • Julie M. Birkholz
  • Rena Bakhshi
  • Ravindra Harige
  • Maarten van Steen
  • Peter Groenewegen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7710)


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.


Social Network Network Dynamic Link Prediction Large Social Network Coauthorship Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Julie M. Birkholz
    • 1
  • Rena Bakhshi
    • 2
  • Ravindra Harige
    • 2
  • Maarten van Steen
    • 2
  • Peter Groenewegen
    • 1
  1. 1.Organization Sciences Department, Network InstituteVU University AmsterdamThe Netherlands
  2. 2.Computer Science Department, Network InstituteVU University AmsterdamThe Netherlands

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