Power-law Distributions in Information Science - Making the Case for Logarithmic Binning

Abstract

We suggest partial logarithmic binning as the method of choice for uncovering the nature of many distributions encountered in information science (IS). Logarithmic binning retrieves information and trends "not visible" in noisy power-law tails. We also argue that obtaining the exponent from logarithmically binned data using a simple least square method is in some cases warranted in addition to methods such as the maximum likelihood. We also show why often used cumulative distributions can make it difficult to distinguish noise from genuine features, and make it difficult to obtain an accurate power-law exponent of the underlying distribution. The treatment is non-technical, aimed at IS researchers with little or no background in mathematics.

Accepted for publication in JASIST

References in corpus (3)

• Power-law distributions in empirical data
• Power laws, Pareto distributions and Zipf's law
• Modes of Collaboration in Modern Science - Beyond Power Laws and Preferential Attachment

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