  We propose a model to analyze citation growth and influences of fitness
(competitiveness) factors in an evolving citation network. Applying the
proposed method to modeling citations to papers and scholars in the InfoVis
2004 data, a benchmark collection about a 31-year history of information
visualization, leads to findings consistent with citation distributions in
general and observations of the domain in particular. Fitness variables based
on prior impacts and the time factor have significant influences on citation
outcomes. We find considerably large effect sizes from the fitness modeling,
which suggest inevitable bias in citation analysis due to these factors. While
raw citation scores offer little insight into the growth of InfoVis,
normalization of the scores by influences of time and prior fitness offers a
reasonable depiction of the field's development. The analysis demonstrates the
proposed model's ability to produce results consistent with observed data and
to support meaningful comparison of citation scores over time.

Applications;Digital Libraries;Social and Information Networks;Physics and Society