Characterizing and modeling citation dynamics

Details Characterizing and modeling citation dynamics Characterizing and modeling citation dynamics

2011-10-10

arxiv.org/abs/1110.2153v1

PLoS ONE 6(9): e24926 (2011)

Physics

Physics and Society

8 pages, 5 figures

Scientific paper

10.1371/journal.pone.0024926

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.

Affiliated with

Also associated with

No associations

Rating

Characterizing and modeling citation dynamics does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Characterizing and modeling citation dynamics, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Characterizing and modeling citation dynamics will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-148034