Graph model selection using maximum likelihood

  title={Graph model selection using maximum likelihood},
  author={Ivona Bez{\'a}kov{\'a} and Adam Tauman Kalai and Rahul Santhanam},
In recent years, there has been a proliferation of theoretical graph models, e.g., preferential attachment and small-world models, motivated by real-world graphs such as the Internet topology. To address the natural question of which model is best for a particular data set, we propose a model selection criterion for graph models. Since each model is in fact a probability distribution over graphs, we suggest using Maximum Likelihood to compare graph models and select their parameters… CONTINUE READING
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University of Wisconsin at Madison

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National Laboratory for Applied Network Research Routing data

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