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

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

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

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; 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.

Original languageEnglish
Pages (from-to)19-35
Number of pages17
JournalArtificial Intelligence
Volume240
DOIs
Publication statusPublished - 1 Nov 2016

Keywords

  • AI's 10 to watch
  • Citation index
  • Exact algorithms
  • Hirsch index
  • Parameterized complexity

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