The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions
Open accessAbstract
Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years, effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible.
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Cites
Structural Properties of the Caenorhabditis elegans Neuronal Network
Varshney, L. R., Chen, B. L., Paniagua, E., Hall, D. H., & Chklovskii, D. B. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001066 · Cited by 867
The Rich Club of the C. elegans Neuronal Connectome
Towlson, E. K., Vértes, P. E., Ahnert, S. E., Schafer, W. R., & Bullmore, E. T. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3784-12.2013 · Cited by 343 · cited 1×
“The second, moderately fitting dataset is the number of connections each neuron has in the nematode worm C. elegans [8,9].”
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Cited by
Network analysis of synthesizable materials discovery
Aykol, M., Hegde, V. I., Hung, L., Suram, S., Herring, P., Wolverton, C., & Hummelshøj, J. S. (2019) · Nature Communications · doi:10.1038/s41467-019-10030-5 · Cited by 120
Social media networks, fake news, and polarization
Azzimonti, M., & Fernandes, M. (2023) · European Journal of Political Economy · doi:10.1016/j.ejpoleco.2022.102256 · Cited by 111
Corruption risk in contracting markets: a network science perspective
Wachs, J., Fazekas, M., & Kertész, J. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-019-00204-1 · Cited by 73
Yang, Y., Li, G., Zhong, Y., Xu, Q., Chen, B.-J., Lin, Y.-T., Chapkin, R. S., & Cai, J. J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad450 · Cited by 48
Radiolysis Generates a Complex Organosynthetic Chemical Network
Adam, Z., Fahrenbach, A. C., Jacobson, S. M., Kacar, B., & Zubarev, D. Y. (2019) · doi:10.26434/chemrxiv.10148684 · Cited by 34
Metadata
- Authors
- Alstott, J., Bullmore, E., & Plenz, D.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-01-29
- Volume
- 9
- Issue
- 1
- Pages
- e85777
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2088856850
- Cited by
- 1,002
- References
- 2
- Type
- software paper
- Language
- en
- Open access
- gold
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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.
