Analysis of effects to scientific impact indicators based on the coevolution of coauthorship and citation networks

TL;DR

This paper models coauthorship and citation networks to analyze how various parameters affect scientific impact indicators like impact factor and h-index, revealing potential manipulation avenues and emphasizing the need for refined metrics.

Contribution

It introduces a joint coauthorship and citation network model based on preferential attachment, allowing analysis of parameter effects on impact indicators.

Findings

Increasing reference number N boosts impact factor and h-index.

Reducing paper lifetime {} increases impact factor and h-index.

enlarging team size m increases h-index without new authors.

Abstract

While computer modeling and simulation are crucial for understanding scientometrics, their practical use in literature remains somewhat limited. In this study, we establish a joint coauthorship and citation network using preferential attachment. As papers get published, we update the coauthorship network based on each paper's author list, representing the collaborative team behind it. This team is formed considering the number of collaborations each author has, and we introduce new authors at a fixed probability, expanding the coauthorship network. Simultaneously, as each paper cites a specific number of references, we add an equivalent number of citations to the citation network upon publication. The likelihood of a paper being cited depends on its existing citations, fitness value, and age. Then we calculate the journal impact factor and h-index, using them as examples of scientific impact indicators. After thorough validation, we conduct case studies to analyze the impact of different parameters on the journal impact factor and h-index. The findings reveal that increasing the reference number N or reducing the paper's lifetime {\theta} significantly boosts the journal impact factor and average h-index. On the other hand, enlarging the team size m without introducing new authors or decreasing the probability of newcomers p notably increases the average h-index. In conclusion, it is evident that various parameters influence scientific impact indicators, and their interpretation can be manipulated by authors. Thus, exploring the impact of these parameters and continually refining scientific impact indicators are essential. The modeling and simulation method serves as a powerful tool in this ongoing process, and the model can be easily extended to include other scientific impact indicators and scenarios.