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

IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. ed. / Michael Wooldridge; Qiang Yang. International Joint Conferences on Artificial Intelligence, 2015. p. 808-814 (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2015-January).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Harvard

Van Bevern, R, Komusiewicz, C, Niedermeier, R, Sorge, M & Walsh, T 2015, H-index manipulation by merging articles: Models, theory, and experiments. in M Wooldridge & Q Yang (eds), IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. IJCAI International Joint Conference on Artificial Intelligence, vol. 2015-January, International Joint Conferences on Artificial Intelligence, pp. 808-814, 24th International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, 25.07.2015.

APA

Van Bevern, R., Komusiewicz, C., Niedermeier, R., Sorge, M., & Walsh, T. (2015). H-index manipulation by merging articles: Models, theory, and experiments. In M. Wooldridge, & Q. Yang (Eds.), IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence (pp. 808-814). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2015-January). International Joint Conferences on Artificial Intelligence.

Vancouver

Van Bevern R, Komusiewicz C, Niedermeier R, Sorge M, Walsh T. H-index manipulation by merging articles: Models, theory, and experiments. In Wooldridge M, Yang Q, editors, IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence. 2015. p. 808-814. (IJCAI International Joint Conference on Artificial Intelligence).

Author

Van Bevern, René ; Komusiewicz, Christian ; Niedermeier, Rolf et al. / H-index manipulation by merging articles: Models, theory, and experiments. IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. editor / Michael Wooldridge ; Qiang Yang. International Joint Conferences on Artificial Intelligence, 2015. pp. 808-814 (IJCAI International Joint Conference on Artificial Intelligence).

BibTeX

@inproceedings{361e9132f5b348fdbfe28527cbe3d914,
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, 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.",
author = "{Van Bevern}, Ren{\'e} and Christian Komusiewicz and Rolf Niedermeier and Manuel Sorge and Toby Walsh",
note = "Supported by the DFG, project DAPA (NI 369/12). Main work done during a visit at TU Berlin while supported by the Alexander von Humboldt Foundation, Bonn, Germany.; 24th International Joint Conference on Artificial Intelligence, IJCAI 2015 ; Conference date: 25-07-2015 Through 31-07-2015",
year = "2015",
month = jan,
day = "1",
language = "English",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",
pages = "808--814",
editor = "Michael Wooldridge and Qiang Yang",
booktitle = "IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence",

}

RIS

TY - GEN

T1 - H-index manipulation by merging articles: Models, theory, and experiments

AU - Van Bevern, René

AU - Komusiewicz, Christian

AU - Niedermeier, Rolf

AU - Sorge, Manuel

AU - Walsh, Toby

N1 - Supported by the DFG, project DAPA (NI 369/12). Main work done during a visit at TU Berlin while supported by the Alexander von Humboldt Foundation, Bonn, Germany.

PY - 2015/1/1

Y1 - 2015/1/1

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, 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.

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, 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.

UR - http://www.scopus.com/inward/record.url?scp=84949792641&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84949792641

T3 - IJCAI International Joint Conference on Artificial Intelligence

SP - 808

EP - 814

BT - IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence

A2 - Wooldridge, Michael

A2 - Yang, Qiang

PB - International Joint Conferences on Artificial Intelligence

T2 - 24th International Joint Conference on Artificial Intelligence, IJCAI 2015

Y2 - 25 July 2015 through 31 July 2015

ER -

ID: 22340007