The DOI matches its Crossref record.
From gene families and genera to incomes and internet file sizes: Why power laws are so common in nature
Abstract
We present a simple explanation for the occurrence of power-law tails in statistical distributions by showing that if stochastic processes with exponential growth in expectation are killed (or observed) randomly, the distribution of the killed or observed state exhibits power-law behavior in one or both tails. This simple mechanism can explain power-law tails in the distributions of the sizes of incomes, cities, internet files, biological taxa, and in gene family and protein family frequencies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Newman (2005) · doi:10.1080/00107510500052444 · Cited by 5,749
A Brief History of Generative Models for Power Law and Lognormal Distributions
Mitzenmacher, M. (2004) · Internet Mathematics · doi:10.1080/15427951.2004.10129088 · Cited by 1,188
COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings
Laguarta, J., Hueto, F., & Subirana, B. (2020) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2020.3026928 · Cited by 589
Being Critical of Criticality in the Brain
Beggs, J. M., & Timme, N. (2012) · Frontiers in Physiology · doi:10.3389/fphys.2012.00163 · Cited by 494
Quantitative and empirical demonstration of the Matthew effect in a study of career longevity
Petersen, A. M., Jung, W.-S., Yang, J.-S., & Stanley, H. E. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1016733108 · Cited by 228
Metadata
- Authors
- Reed, W. J., & Hughes, B. D.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2002-12-20
- Volume
- 66
- Issue
- 6
- Pages
- 067103-067103
- ISSN
- 1063-651X (ISSN-L)
- 1095-3787
- OpenAlex
- W1998908151
- Cited by
- 278
- References
- 0
- FWCI
- 20.74
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: From gene families and genera to incomes and internet file sizes: Why power laws are so common in nature. https://app.cochat.ai/papers/10.1103/physreve.66.067103
- The paper
- Reed, W. J., & Hughes, B. D. (2002). From gene families and genera to incomes and internet file sizes: Why power laws are so common in nature. Physical Review E. https://doi.org/10.1103/physreve.66.067103
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
