Preprint Code doi abstract bibtex

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) 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 arbitrary compatibility graphs), 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.

@article{BKN+16, title = {H-Index Manipulation by Merging Articles: Models, Theory, and Experiments}, author = {René van Bevern and Christian Komusiewicz and Rolf Niedermeier and Manuel Sorge and Toby Walsh}, url_Preprint = {http://arxiv.org/abs/1412.5498}, url_Code = {http://fpt.akt.tu-berlin.de/hindex/}, year = 2016, date = {2016-11-01}, journal = {Artificial Intelligence}, volume = 240, pages = {19--35}, 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, which 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 arbitrary compatibility graphs), 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.}, doi = {10.1016/j.artint.2016.08.001}, keywords = {network analysis, parameterized complexity} }

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