  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.
