Zipf, Power-laws, and Pareto-a ranking tutorial

By Lada A. Adamic. 2000

351 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3f44bcb1d413e17a0135df9dad901a4b76cad3b4

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Citations in the corpus

Flickr tag recommendation based on collective knowledge (2008, background): “J(ti, tj) := |ti ∩ tj | |ti ∪ tj | (1) The coefficient takes the number of intersections between the two tags, divided by the union of the two tags.”
Characterization of failures in an IP backbone (2004, background): “Examples include man-made or naturally occurring phenomena, such as word frequencies, income distribution, city sizes and earthquake magnitudes, [26].”
Characterization of Failures in an Operational IP Backbone Network (2008, background): “man-made or naturally occurring phenomena, such as word frequencies, income distribution, city sizes, and earthquake magnitudes [28].”
Complexity and Empirical Economics (2005, background): “The rank size rule can be shown to imply that 1 Fx(c) c 1, see Adamic (2003) for a simple derivation, and so has the canonical form of a power law.”
Analyzing peer-to-peer traffic across large networks (2002, methodology): “First, Zip f’s law [16] is widely used to model skewed distributions becaus e of its simple form and has been recently applied to research o n Web caching [17] and Internet topology models [18].”
The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth (2005, background): “Adamic(2000)providedsomesim-pleanalytictoolsbywhichtheslopeb=.466inthisZipfplotcanbeconvertedto 466inthisZipfplotcanbeconvertedto γ = 3.15, the slope of the corresponding probability distribution in log–log coordinates.”
Graph evolution: Densification and shrinking diameters (2006, background): “The actual slope of the plotted line is 1/( 1), which is the relation between the power-law exponent and the slope of the rank degree plot (see [ 4 ] for more details on these relationships).”
Evolution of networks (2001, background): “s about 1.8. Note that the power-law dependence seems not very pronounced in this case. A power-law dependence was indicated at the distribution of the number of visits (connections) to the Web sites [119]. The value of the corresponding exponent was estimated as 2.0. The ﬁt is rather poor. One shou”
Power laws and the AS-level internet topology (2003, background): “It can be shown that the exponents of the two power-laws are related [61] [11][29].”
Disinformation and social bot operations in the run up to the 2017 French presidential election (2017, background): “Word distributions enjoy some characteristic distributions, e.g., the Zipf law that suggests that the ranked word frequency should obey a Zipfian distribution (Adamic, 2000).”