  There has been a long history of research into the structure and evolution of
mankind's scientific endeavor. However, recent progress in applying the tools
of science to understand science itself has been unprecedented because only
recently has there been access to high-volume and high-quality data sets of
scientific output (e.g., publications, patents, grants), as well as computers
and algorithms capable of handling this enormous stream of data. This paper
reviews major work on models that aim to capture and recreate the structure and
dynamics of scientific evolution. We then introduce a general process model
that simultaneously grows co-author and paper-citation networks. The
statistical and dynamic properties of the networks generated by this model are
validated against a 20-year data set of articles published in the Proceedings
of the National Academy of Science. Systematic deviations from a power law
distribution of citations to papers are well fit by a model that incorporates a
partitioning of authors and papers into topics, a bias for authors to cite
recent papers, and a tendency for authors to cite papers cited by papers that
they have read. In this TARL model (for Topics, Aging, and Recursive Linking),
the number of topics is linearly related to the clustering coefficient of the
simulated paper citation network.
