Visualizing Collaboration with Superstars

A Novel Approach to Visualizing Collaboration

Bachelor thesis (2024)

Authors

P.S. Hull Electrical Engineering, Mathematics and Computer Science

Contributors

H.S. Hung Pattern Recognition and Bioinformatics - EEMCS (mentor)
V. Agarwal Pattern Recognition and Bioinformatics - EEMCS (mentor)
C. Hao Pattern Recognition and Bioinformatics - EEMCS (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science
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Published Date

25-06-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Superstar researchers - those who author research papers which are far more widely cited than average - are generally well-respected within their fields and are frequently sought by new researchers for advice on career development and for collaborations. Though the effect of collaboration with superstar researchers on associated researchers is not well-researched yet, it is believed that collaboration with superstar researchers increases the research output of associated researchers, but may decrease their originality and innovation. This project explores a method of visualizing the career development of researchers associated with superstars. Using a dataset of authors, papers, and paper citations, a graph has been created with papers as edges and nodes as authors to visualize the career development of associate authors after collaboration with a superstar. The utility of this visualization is evaluated using heuristics.