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Zipf's Law Leads to Heaps' Law: Analyzing Their Relation in Finite-Size Systems

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  • Linyuan Lü
  • Zi-Ke Zhang
  • Tao Zhou

Abstract

Background: Zipf's law and Heaps' law are observed in disparate complex systems. Of particular interests, these two laws often appear together. Many theoretical models and analyses are performed to understand their co-occurrence in real systems, but it still lacks a clear picture about their relation. Methodology/Principal Findings: We show that the Heaps' law can be considered as a derivative phenomenon if the system obeys the Zipf's law. Furthermore, we refine the known approximate solution of the Heaps' exponent provided the Zipf's exponent. We show that the approximate solution is indeed an asymptotic solution for infinite systems, while in the finite-size system the Heaps' exponent is sensitive to the system size. Extensive empirical analysis on tens of disparate systems demonstrates that our refined results can better capture the relation between the Zipf's and Heaps' exponents. Conclusions/Significance: The present analysis provides a clear picture about the relation between the Zipf's law and Heaps' law without the help of any specific stochastic model, namely the Heaps' law is indeed a derivative phenomenon from the Zipf's law. The presented numerical method gives considerably better estimation of the Heaps' exponent given the Zipf's exponent and the system size. Our analysis provides some insights and implications of real complex systems. For example, one can naturally obtained a better explanation of the accelerated growth of scale-free networks.

Suggested Citation

  • Linyuan Lü & Zi-Ke Zhang & Tao Zhou, 2010. "Zipf's Law Leads to Heaps' Law: Analyzing Their Relation in Finite-Size Systems," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-11, December.
  • Handle: RePEc:plo:pone00:0014139
    DOI: 10.1371/journal.pone.0014139
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    Cited by:

    1. Tunnicliffe, Martin & Hunter, Gordon, 2022. "Random sampling of the Zipf–Mandelbrot distribution as a representation of vocabulary growth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    2. François Lafond & Daniel Kim, 2019. "Long-run dynamics of the U.S. patent classification system," Journal of Evolutionary Economics, Springer, vol. 29(2), pages 631-664, April.
    3. Xi, Ning & Zhang, Zi-Ke & Zhang, Yi-Cheng & Ge, Zehui & She, Li & Zhang, Kui, 2014. "Cultural evolution: The case of babies’ first names," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 139-144.

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