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H-index manipulation by merging articles : Models, theory, and experiments. / van Bevern, René; Komusiewicz, Christian; Niedermeier, Rolf et al.

In: Artificial Intelligence, Vol. 240, 01.11.2016, p. 19-35.

Research output: Contribution to journal › Article › peer-review

Harvard

van Bevern, R, Komusiewicz, C, Niedermeier, R, Sorge, M & Walsh, T 2016, 'H-index manipulation by merging articles: Models, theory, and experiments', Artificial Intelligence, vol. 240, pp. 19-35. https://doi.org/10.1016/j.artint.2016.08.001

APA

van Bevern, R., Komusiewicz, C., Niedermeier, R., Sorge, M., & Walsh, T. (2016). H-index manipulation by merging articles: Models, theory, and experiments. Artificial Intelligence, 240, 19-35. https://doi.org/10.1016/j.artint.2016.08.001

Vancouver

van Bevern R, Komusiewicz C, Niedermeier R, Sorge M, Walsh T. H-index manipulation by merging articles: Models, theory, and experiments. Artificial Intelligence. 2016 Nov 1;240:19-35. doi: 10.1016/j.artint.2016.08.001

Author

van Bevern, René ; Komusiewicz, Christian ; Niedermeier, Rolf et al. / H-index manipulation by merging articles : Models, theory, and experiments. In: Artificial Intelligence. 2016 ; Vol. 240. pp. 19-35.

BibTeX

@article{94c7d1af201146968c968b40012cdd2b,
title = "H-index manipulation by merging articles: Models, theory, and experiments",
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; this 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 compatibility graphs that are cliques), 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.",
keywords = "AI's 10 to watch, Citation index, Exact algorithms, Hirsch index, Parameterized complexity",
author = "{van Bevern}, Ren{\'e} and Christian Komusiewicz and Rolf Niedermeier and Manuel Sorge and Toby Walsh",
year = "2016",
month = nov,
day = "1",
doi = "10.1016/j.artint.2016.08.001",
language = "English",
volume = "240",
pages = "19--35",
journal = "Artificial Intelligence",
issn = "0004-3702",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - H-index manipulation by merging articles

T2 - Models, theory, and experiments

AU - van Bevern, René

AU - Komusiewicz, Christian

AU - Niedermeier, Rolf

AU - Sorge, Manuel

AU - Walsh, Toby

PY - 2016/11/1

Y1 - 2016/11/1

N2 - 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; this 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 compatibility graphs that are cliques), 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.

AB - 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; this 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 compatibility graphs that are cliques), 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.

KW - AI's 10 to watch

KW - Citation index

KW - Exact algorithms

KW - Hirsch index

KW - Parameterized complexity

UR - http://www.scopus.com/inward/record.url?scp=84983429819&partnerID=8YFLogxK

U2 - 10.1016/j.artint.2016.08.001

DO - 10.1016/j.artint.2016.08.001

M3 - Article

AN - SCOPUS:84983429819

VL - 240

SP - 19

EP - 35

JO - Artificial Intelligence

JF - Artificial Intelligence

SN - 0004-3702

ER -

ID: 22339560