  A common consensus in the literature is that the citation profile of
published articles in general follows a universal pattern - an initial growth
in the number of citations within the first two to three years after
publication followed by a steady peak of one to two years and then a final
decline over the rest of the lifetime of the article. This observation has long
been the underlying heuristic in determining major bibliometric factors such as
the quality of a publication, the growth of scientific communities, impact
factor of publication venues etc. In this paper, we gather and analyze a
massive dataset of scientific papers from the computer science domain and
notice that the citation count of the articles over the years follows a
remarkably diverse set of patterns - a profile with an initial peak (PeakInit),
with distinct multiple peaks (PeakMul), with a peak late in time (PeakLate),
that is monotonically decreasing (MonDec), that is monotonically increasing
(MonIncr) and that can not be categorized into any of the above (Oth). We
conduct a thorough experiment to investigate several important characteristics
of these categories such as how individual categories attract citations, how
the categorization is influenced by the year and the venue of publication of
papers, how each category is affected by self-citations, the stability of the
categories over time, and how much each of these categories contribute to the
core of the network. Further, we show that the traditional preferential
attachment models fail to explain these citation profiles. Therefore, we
propose a novel dynamic growth model that takes both the preferential
attachment and the aging factor into account in order to replicate the
real-world behavior of various citation profiles. We believe that this paper
opens the scope for a serious re-investigation of the existing bibliometric
indices for scientific research.
