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Power law distributions in information science: Making the case for logarithmic binning

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Journal of the American Society for Information Science and Technology, Volume 61, Issue 12

Pages 2417 - 2425

Published: 01 December 2010 Publication History

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 to obtain an accurate power law exponent of the underlying distribution. The treatment is nontechnical, aimed at IS researchers with little or no background in mathematics. © 2010 Wiley Periodicals, Inc.

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Journal of the American Society for Information Science and Technology Volume 61, Issue 12

December 2010

216 pages

ISSN:1532-2882

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John Wiley & Sons, Inc.

United States

Publication History

Published: 01 December 2010

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https://dl.acm.org/doi/10.1145/3604915.3608773
Fan ZXu KDong ZPeng HZhang JYu PChen HDuh WHuang HKato MMothe JPoblete B(2023)Graph Collaborative Signals Denoising and Augmentation for RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591994(2037-2041)Online publication date: 19-Jul-2023
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