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Generating random networks from a given distribution

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  • Carter, Nathan
  • Hadlock, Charles
  • Haughton, Dominique

Abstract

Several variations are given for an algorithm that generates random networks approximately respecting the probabilities given by any likelihood function, such as from a p* social network model. A novel use of the genetic algorithm is incorporated in these methods, which improves its applicability to the degenerate distributions that can arise with p* models. Our approach includes a convenient way to find the high-probability items of an arbitrary network distribution function.

Suggested Citation

  • Carter, Nathan & Hadlock, Charles & Haughton, Dominique, 2008. "Generating random networks from a given distribution," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3928-3938, April.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:8:p:3928-3938
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    References listed on IDEAS

    as
    1. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2008. "Goodness of Fit of Social Network Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 248-258, March.
    2. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
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