## RenĂ© van Bevern, Christian Komusiewicz, Rolf Niedermeier, Manuel Sorge, and
Toby Walsh.
H-index manipulation by merging articles: Models, theory, and
experiments.
*Artificial Intelligence*, 240:19–35, 2016.

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) computational
complexity of maximizing the H-index using article
merges. Herein, to model realistic manipulation
scenarios, we define a compatibility graph whose edges
correspond to plausible merges. Moreover, we consider
several different 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 (that is, for
arbitrary compatibility graphs), 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 if
one merges articles with highly dissimilar titles.

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