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

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...

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Vydáno v:Artificial intelligence Ročník 240; s. 19 - 35
Hlavní autoři: van Bevern, René, Komusiewicz, Christian, Niedermeier, Rolf, Sorge, Manuel, Walsh, Toby
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.11.2016
Témata:
AI's 10 to watch
Artificial intelligence
Citation index
Compatibility
Computation
Exact algorithms
Expert systems
Graphs
Hirsch index
Mathematical models
Merging
Parameterized complexity
Scientific papers
Hirsch index
Citation index
Parameterized complexity
Exact algorithms
AI's 10 to watch
ISSN:0004-3702, 1872-7921
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2016.08.001