Collaborative Research: Statistical Modeling and Inference on Directed Network Data for Understanding Faculty Hiring Dynamics

About This Grant

This research will advance the progress of science by creating a set of new statistical tools for analyzing complex networks that are fundamental to the nation's prosperity and welfare. Understanding the underlying structures of these networks is critical for making informed, data-driven decisions to promote better and higher productivity in academia. This project will analyze the complex networks of faculty hiring between U.S. universities to understand how factors such as institutional research productivity, geography, and field of study influence hiring dynamics. The outcomes will enhance the efficiency of the U.S. academic system and provide valuable insights for researchers and policymakers. A key guiding principle of this project is a commitment to broad engagement; all outreach, recruitment, and participatory activities are designed to be fully open to all Americans. The project will also create a faculty hiring dataset with open access to the public, release all new methods in a free software package, and develop training opportunities for the next generation of American data scientists. From a technical perspective, this research will create a versatile statistical toolkit for analyzing weighted, directed networks, which pose significant challenges for existing methods. The investigators will develop four novel methodologies designed for commonly seen applications in analyzing the hiring networks. First, the project will establish a network-to-covariate regression model to handle count-based network data while accounting for complex dependencies between connections. Second, the research will introduce a nonparametric testing framework using network U-statistics to rigorously test for dependence structures. Third, a new method will be developed to identify and perform inference on "core-periphery" structures, allowing researchers to distinguish informative patterns from non-informative ones. Finally, the project will introduce a conformal inference framework to formally compare entire populations of networks, even when the networks differ in size. These new statistical methods will be validated through simulation and applied to the comprehensive faculty hiring network dataset, with results disseminated through peer-reviewed publications and the project's open-source software. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.