Problems with fitting to the power-law distribution

  • M. L. Goldstein
  • S. A. Morris
  • G. G. YenEmail author


This short communication uses a simple experiment to show that fitting to a power law distribution by using graphical methods based on linear fit on the log-log scale is biased and inaccurate. It shows that using maximum likelihood estimation (MLE) is far more robust. Finally, it presents a new table for performing the Kolmogorov-Smirnov test for goodness-of-fit tailored to power-law distributions in which the power-law exponent is estimated using MLE. The techniques presented here will advance the application of complex network theory by allowing reliable estimation of power-law models from data and further allowing quantitative assessment of goodness-of-fit of proposed power-law models to empirical data.


Empirical Data Maximum Likelihood Estimation Likelihood Estimation Complex Network Reliable Estimation 
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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© Springer-Verlag Berlin/Heidelberg 2004

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringOklahoma State UniversityStillwaterUSA

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