Characterizing and modeling citation dynamics

TL;DR

This paper analyzes citation distributions in physics journals, finding that shifted power laws best describe the data, and proposes a model incorporating bursty citation dynamics with preferential attachment.

Contribution

It introduces a new model of citation dynamics with time-dependent attractiveness that accurately reproduces empirical distributions and burst phenomena.

Findings

Shifted power law best fits citation data across time spans.

Citation bursts are common and vary widely in size.

The proposed model captures both distribution and burst behavior.

Abstract

Citation distributions are crucial for the analysis and modeling of the activity of scientists. We investigated bibliometric data of papers published in journals of the American Physical Society, searching for the type of function which best describes the observed citation distributions. We used the goodness of fit with Kolmogorov-Smirnov statistics for three classes of functions: log-normal, simple power law and shifted power law. The shifted power law turns out to be the most reliable hypothesis for all citation networks we derived, which correspond to different time spans. We find that citation dynamics is characterized by bursts, usually occurring within a few years since publication of a paper, and the burst size spans several orders of magnitude. We also investigated the microscopic mechanisms for the evolution of citation networks, by proposing a linear preferential attachment with time dependent initial attractiveness. The model successfully reproduces the empirical citation distributions and accounts for the presence of citation bursts as well.