Growing complex network of citations of scientific papers: Modeling and measurements

Michael Golosovsky, Sorin Solomon

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

We consider the network of citations of scientific papers and use a combination of the theoretical and experimental tools to uncover microscopic details of this network growth. Namely, we develop a stochastic model of citation dynamics based on the copying-redirection-triadic closure mechanism. In a complementary and coherent way, the model accounts both for statistics of references of scientific papers and for their citation dynamics. Originating in empirical measurements, the model is cast in such a way that it can be verified quantitatively in every aspect. Such validation is performed by measuring citation dynamics of physics papers. The measurements revealed nonlinear citation dynamics, the nonlinearity being intricately related to network topology. The nonlinearity has far-reaching consequences including nonstationary citation distributions, diverging citation trajectories of similar papers, runaways or "immortal papers" with infinite citation lifetime, etc. Thus nonlinearity in complex network growth is our most important finding. In a more specific context, our results can be a basis for quantitative probabilistic prediction of citation dynamics of individual papers and of the journal impact factor.

Original languageEnglish
Article number012324
JournalPhysical Review E
Volume95
Issue number1
DOIs
StatePublished - 30 Jan 2017

Bibliographical note

Publisher Copyright:
© 2017 American Physical Society.

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