Subnormal Distribution Derived From Evolving Networks With Variable Elements

Minyu Feng, Hong Qu, Zhang Yi, Jurgen Kurths

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


During the past decades, power-law distributions have played a significant role in analyzing the topology of scale-free networks. However, in the observation of degree distributions in practical networks and other nonuniform distributions such as the wealth distribution, we discover that, there exists a peak at the beginning of most real distributions, which cannot be accurately described by a monotonic decreasing power-law distribution. To better describe the real distributions, in this paper, we propose a subnormal distribution derived from evolving networks with variable elements and study its statistical properties for the first time. By utilizing this distribution, we can precisely describe those distributions commonly existing in the real world, e.g., distributions of degree in social networks and personal wealth. Additionally, we fit connectivity in evolving networks and the data observed in the real world by the proposed subnormal distribution, resulting in a better performance of fitness.

Original languageEnglish
Pages (from-to)2556 - 2568
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number9
Early online date2 Oct 2017
Publication statusPublished - Sept 2018

Bibliographical note

This work was supported by the National Science Foundation of China under Grant 61573081, Grant 61432012, and Grant U1435213.


  • Degree distribution
  • evolving networks
  • Gibrat’s law
  • power-law distribution
  • probability theory


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