## RenĂ© van Bevern, Christian Komusiewicz, Rolf Niedermeier, Manuel Sorge, and
Toby Walsh.
H-index manipulation by merging articles: Models, theory, and
experiments.
In *Proceedings of the 24th International Joint Conference on
Artificial Intelligence, IJCAI'15, Buenos Aires, Argentina*, pages 808–814.
AAAI Press, 2015.
The final version of this article appeared in Artificial
Intelligence.

An author's profile on Google Scholar consists of
indexed articles and associated data, such as the
number of citations and the H-index. The author is
allowed to merge articles, which may affect the
H-index. We analyze the parameterized complexity of
maximizing the H-index using article merges. Herein,
to model realistic manipulation scenarios, we define a
compatability graph whose edges correspond to
plausible merges. Moreover, we consider multiple
possible measures for computing the citation count of
a merged article. For the measure used by Google
Scholar, we give an algorithm that maximizes the
H-index in linear time if the compatibility graph has
constant-size connected components. In contrast, if we
allow to merge arbitrary articles, then already
increasing the H-index by one is NP-hard. Experiments
on Google Scholar profiles of AI researchers show that
the H-index can be manipulated substantially only by
merging articles with highly dissimilar titles, which
would be easy to discover.

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