---
title: "H-Index Manipulation by Merging Articles: Models, Theory, and Experiments"
authors:
  - "René van Bevern"
  - "Christian Komusiewicz"
  - "Rolf Niedermeier"
  - "Manuel Sorge"
  - "Toby Walsh"
doi: ""
url: "https://ijcai.org/proceedings/2015/119"
pdf: "https://ijcai.org/proceedings/2015/0119.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "808-813"
track: "Main Track"
keywords:
---

# H-Index Manipulation by Merging Articles: Models, Theory, and Experiments

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

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 808-813.

## 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 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.