ClausetShaliziNewman

Clauset, Shalizi, and Newman—a trio of researchers whose collaborative work is widely associated with the name ClausetShaliziNewman—produced a foundational methodological study on power-law distributions in empirical data. Published in 2009 in SIAM Review, the paper presents a practical framework for detecting and characterizing power-law behavior in real-world datasets.

Core contributions include estimating the power-law exponent and the lower bound xmin using maximum likelihood methods, and assessing goodness-of-fit with a Kolmogorov–Smirnov statistic, whose significance is evaluated via Monte Carlo simulations. The authors emphasize distinguishing genuine power-law behavior from approximate fits, and they provide procedures to compare power-law models with alternative heavy-tailed distributions (such as log-normal, exponential) using likelihood-ratio tests.

The work has had wide influence across disciplines, becoming a standard reference in data analysis for physics, network science, economics, biology, and beyond. It promoted a formal, repeatable approach to fitting and testing power laws and spurred the development of open-source tools implementing the methodology. Its emphasis on rigorous statistical testing helped counter many earlier claims of power-law behavior based on visual inspection alone.

Authorship and affiliations: Aaron Clauset is a computer scientist at the University of Colorado Boulder; Cosma Shalizi was affiliated with Carnegie Mellon University; Mark Newman is a physicist at the University of Michigan. The collaboration is noted for its clear, data-driven framework and for articulating when data do not support a power-law model.