Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
H-index manipulation by merging articles: Models, theory, and experiments. / Van Bevern, René; Komusiewicz, Christian; Niedermeier, Rolf и др.
IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. ред. / Michael Wooldridge; Qiang Yang. International Joint Conferences on Artificial Intelligence, 2015. стр. 808-814 (IJCAI International Joint Conference on Artificial Intelligence; Том 2015-January).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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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