TY - GEN
T1 - H-index manipulation by merging articles
T2 - 24th International Joint Conference on Artificial Intelligence, IJCAI 2015
AU - Van Bevern, René
AU - Komusiewicz, Christian
AU - Niedermeier, Rolf
AU - Sorge, Manuel
AU - Walsh, Toby
N1 - Funding Information:
René van Bevern was supported by the German Research Foundation (DFG), project DAPA ( NI 369/12 ), at TU Berlin, and by the Russian Foundation for Basic Research (RFBR), project 16-31-60007 mol_a_dk , at Novosibirsk State University. Christian Komusiewicz was supported by the DFG project MAGZ ( KO 3669/4-1 ) and Manuel Sorge was supported by the DFG project DAPA ( NI 369/12 ). Toby Walsh was supported by the Alexander von Humboldt Foundation , Bonn, Germany, while at TU Berlin. The main work was done while Toby Walsh was affiliated with University of New South Wales and Data61, Sydney, Australia.
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
Y2 - 25 July 2015 through 31 July 2015
ER -