Unzipping Zipf's law

Abstract

of the RGF model, rather than a correction introduced to fit the data. The RGF model just as easily fits word-frequency distributions representing entire books as it fits random subsamples of the same texts, something that alternative models generally cannot do. Finally, the approach is flexible enough to incorporate system-specific constraints, as needed. The work of Baek and colleagues 1 may be the first to provide a truly general explanation of the prevalence of power-law distributions in frequency counts. But it is not yet ready to replace other models entirely. For many, if not all, systems the intuition behind the assumption that one wishes to minimize the information cost of locating an item needs to be further developed. By contrast, growth models usually integrate intuition about a system's evolution. Furthermore, the power-law exponents produced by the RGF model in some cases differ from those estimated previously using maximum-likelihood fits to data 2. Nevertheless, by deriving power-law distributions from very general system-independent principles, Baek et al. have raised the bar for other models. A model purporting to explain a power-law distribution should be as general as Baek and colleagues' model, or it should be able to reproduce additional features of the system it models, beyond the familiar straight line on a log-log plot. ■ Lada Adamic is in the School of Information and the Center for the Study