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

René van Bevern, Christian Komusiewicz, Rolf Niedermeier, Manuel Sorge, Toby Walsh

Результат исследования: Научные публикации в периодических изданияхстатья

9 Цитирования (Scopus)

Аннотация

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.

Язык оригиналаанглийский
Страницы (с-по)19-35
Число страниц17
ЖурналArtificial Intelligence
Том240
DOI
СостояниеОпубликовано - 1 ноя 2016

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