  The basic indicators of a researcher's productivity and impact are still the
number of publications and their citation counts. These metrics are clear,
straightforward, and easy to obtain. When a ranking of scholars is needed, for
instance in grant, award, or promotion procedures, their use is the fastest and
cheapest way of prioritizing some scientists over others. However, due to their
nature, there is a danger of oversimplifying scientific achievements.
Therefore, many other indicators have been proposed including the usage of the
PageRank algorithm known for the ranking of webpages and its modifications
suited to citation networks. Nevertheless, this recursive method is
computationally expensive and even if it has the advantage of favouring
prestige over popularity, its application should be well justified,
particularly when compared to the standard citation counts. In this study, we
analyze three large datasets of computer science papers in the categories of
artificial intelligence, software engineering, and theory and methods and apply
12 different ranking methods to the citation networks of authors. We compare
the resulting rankings with self-compiled lists of outstanding researchers
selected as frequent editorial board members of prestigious journals in the
field and conclude that there is no evidence of PageRank-based methods
outperforming simple citation counts.

Digital Libraries;Social and Information Networks;Physics and Society