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Related Experiment Video

Updated: May 28, 2026

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

Characterizing and modeling citation dynamics.

Young-Ho Eom1, Santo Fortunato

  • 1Complex Networks and Systems Lagrange Laboratory, Institute for Scientific Interchange, Torino, Italy.

Plos One
|October 4, 2011
PubMed
Summary
This summary is machine-generated.

A shifted power law best describes scientific citation distributions, revealing citation dynamics characterized by bursts. This finding aids in modeling scientist activity and citation network evolution.

Related Experiment Videos

Last Updated: May 28, 2026

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

Area of Science:

  • Bibliometrics and Scientometrics
  • Physics
  • Network Science

Background:

  • Citation distributions are fundamental for analyzing scientist activity and modeling scientific impact.
  • Understanding citation patterns is key to comprehending the dynamics of scientific progress.

Purpose of the Study:

  • To identify the best-fitting function for describing citation distributions in scientific publications.
  • To investigate the underlying mechanisms driving the evolution of citation networks.

Main Methods:

  • Analysis of bibliometric data from American Physical Society journals.
  • Application of goodness-of-fit tests using Kolmogorov-Smirnov statistics.
  • Comparison of log-normal, simple power law, and shifted power law functions.

Main Results:

  • The shifted power law function provided the most reliable fit for citation distributions across different time spans.
  • Citation dynamics exhibit bursts, typically within a few years of publication, with wide variations in burst size.
  • A proposed model of linear preferential attachment with time-dependent initial attractiveness successfully replicated empirical distributions and burst phenomena.

Conclusions:

  • The shifted power law is a robust model for scientific citation distributions.
  • Citation bursts are an inherent feature of scientific communication dynamics.
  • The developed preferential attachment model offers insights into citation network evolution.