H-Index Manipulation by Merging Articles: Models, Theory, and Experiments. van Bevern, R., Komusiewicz, C., Niedermeier, R., Sorge, M., & Walsh, T. Artificial Intelligence, 240:19–35, 2016. ![H-Index Manipulation by Merging Articles: Models, Theory, and Experiments [link]](https://bibbase.org/img/filetypes/link.svg "link")
Preprint Code doi abstract bibtex 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.
@article{BKN+16,
title = {H-Index Manipulation by Merging Articles: Models,
Theory, and Experiments},
author = {René van Bevern and Christian Komusiewicz and Rolf
Niedermeier and Manuel Sorge and Toby Walsh},
url_Preprint = {http://arxiv.org/abs/1412.5498},
url_Code = {http://fpt.akt.tu-berlin.de/hindex/},
year = 2016,
date = {2016-11-01},
journal = {Artificial Intelligence},
volume = 240,
pages = {19--35},
abstract = {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.},
doi = {10.1016/j.artint.2016.08.001},
keywords = {network analysis, parameterized complexity}
}
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