Statistical Models for Social Networks

  • Stanley Wasserman
  • Philippa Pattison
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Recent developments in statistical models for social networks reflect an increasing theoretical focus in the social and behavioral sciences on the interdependence of social actors in dynamic, network-based social settings (e.g., Abbott, 1997; White, 1992, 1995). As a result, a growing importance has been accorded the problem of modeling the dynamic and complex interdependencies among network ties and the actions of the individuals whom they link. Included in this problem is the identification of cohesive subgroups, or classifications of the individuals. The early focus of statistical network modeling on the mathematical and statistical properties of Bernoulli and dyad-independent random graph distributions has now been replaced by efforts to construct theoretically and empirically plausible parametric models for structural network phenomena and their changes over time.


Social Network Random Graph Dependence Graph Random Graph Model Sociological Methodology 
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 2000

Authors and Affiliations

  • Stanley Wasserman
    • 1
  • Philippa Pattison
    • 2
  1. 1.Department of Psychology and Department of Statistics,Beckman Institute for Advanced Science and TechnologyUniversity of IllinoisChampaignUSA
  2. 2.School of Behavioural Science, Department of PsychologyUniversity of MelbourneParkvilleAustralia

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