Growing complex network of citations of scientific papers -- measurements and modeling

TL;DR

This paper develops a stochastic model of scientific citation network growth incorporating copying, redirection, and triadic closure, revealing nonlinear dynamics that influence citation distributions and the longevity of papers.

Contribution

The paper introduces a comprehensive, empirically verified stochastic model capturing the nonlinear growth mechanisms of citation networks, linking microscopic processes to macroscopic patterns.

Findings

Citation dynamics are nonlinear and related to network topology.

Citation distributions are non-stationary with diverging trajectories.

Some papers become 'immortal' with infinite citation lifetime.

Abstract

To quantify the mechanism of a complex network growth we focus on 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 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 verification 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 non-stationary citation distributions, diverging citation trajectory of similar papers, runaways or "immortal papers" with infinite citation lifetime etc. Thus, our most important finding is nonlinearity in complex network growth. 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.