Who is Hiring Whom: A New Method in Measuring Graduate Programs

In this paper, based on the assumption that “schools tend to hire Ph.D.s from peer or better schools”, we propose a statistical and mathematical approach to rank graduate programs using algorithms deployed on a mutual “hiring graph” among universities. In order to validate our approach, we collect faculty data from the top 50 Computer Science (CS) departments, the top 50 Mechanical Engineering (ME) departments and the top 50 Electrical Engineering (EE) departments across the United States according to U.S. News so as to construct our hiring graph. We refine the PageRank (PR) algorithm and the Hyperlink-Induced Topic Search (HITS) algorithm in order to rank the graduate programs from the hiring graph. Our new rankings are generally consistent with U.S. News rankings, while exposing some new observations about some particular programs. By conducting extensive data analysis, we discover many interesting patterns and facts from our data. Finally, we propose a cross-domain model for graduate program ranking and provide our new graduate program rankings.