University ranking based on faculty hiring network with minimum weighted violation rankings

Liqian Lang (), Yan Wang (), Qinghua Chen and Tao Zheng
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Liqian Lang: School of Systems Science, Beijing Normal University, Beijing 100875, P. R. China
Yan Wang: #x2020;Department of Mathematics, University of California, Los Angeles, Los Angeles 90095, USA
Qinghua Chen: School of Systems Science, Beijing Normal University, Beijing 100875, P. R. China
Tao Zheng: School of Systems Science, Beijing Normal University, Beijing 100875, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 07, 1-12

Abstract: University ranking arouses widespread interest among the society and the scientific community. It can cause resources to be allocated to the entity which has a higher ranking to make tremendous uneven distribution of resources such as funds, faculty, students and so on. Every year various controversial university rankings are issued by different institutions or individuals. However, they have to deal with a huge amount of data and cumbersome computing in their research. Furthermore, during the process of calculation, some key indicators are unreliable, subjective, and difficult to obtain or compute so that their results are easily questioned. An accurate and objective university ranking is important and necessary, but it still remains to be solved. In 2015, Clauset et al. creatively studied university rankings based on faculty hiring network with graduation-employment flow data. They used the minimum violation ranking (MVR) method to get a university ranking which has a high correlation with U.S. News & World Report (USN) and National Research Council (NRC) Ranking, implying a strong consistency between them. This method costs less and is also objective. Inspired by this thought, this paper proposed a new ranking algorithm with minimum weighted violation rankings derived through maximum likelihood estimation. This assumption is more reasonable, and the results are commendably consistent with the rankings of renowned agencies. This more general method is more flexible than non-weighted calculation. More importantly, this work revealed the essential mechanism of MVR by deriving maximum likelihood.

Keywords: University ranking; faculty hiring network; minimum weighted violation rankings; maximum likelihood (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0129183119400175

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