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

TL;DR

This paper investigates the computational complexity of manipulating an author's H-index through article merging, providing algorithms for certain cases and demonstrating NP-hardness in others, with experiments on real profiles.

Contribution

It models realistic article merging scenarios with compatibility graphs and analyzes the complexity of maximizing the H-index, offering algorithms and hardness results.

Findings

Linear-time algorithm for constant-size components

NP-hardness for arbitrary merges

Significant H-index manipulation occurs with similar article titles

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.