Exponential random graph modeling of a faculty hiring network: The IEOR case

Abstract

Faculty hiring networks consist of academic departments in a particular field (vertices) and directed edges from the departments that award Ph.D. degrees to students to the institutions that hires them as faculty. Study of these networks has been used in the past to find a hierarchy, or ranking, among departments, but they can also help reveal sociological aspects of a profession that have consequences in the dissemination of educational innovations and knowledge. In this article, we propose to use a new latent variable Exponential Random Graph Model (ERGM) to study faculty hiring networks. The model uses hierarchy information only as an input to the ERGM, where the hierarchy is obtained by modification of the Minimum Violation Ranking (MVR) method recently suggested in the literature. In contrast to single indices of ranking that can only capture partial features of a complex network, we demonstrate how our latent variable ERGM model provides a clustering of departments that does not necessarily align with the hierarchy as given by the MVR rankings, permits to simplify the network for ease of interpretation, and allows us to reproduce its main characteristics including its otherwise difficult to model presence of directed self-edges, common in faculty hiring networks. Throughout the paper, we illustrate our methods with application to the Industrial/Systems/Operations Research (IEOR) faculty hiring network, not studied before. The IEOR network is contrasted with those previously studied for other related disciplines, such as Computer Science and Business.

Keywords:

• Social networks
• academic analytics
• hierarchical networks
• latent location graph model

Acknowledgements

The authors wish to thank two anonymous referees and the Department Area editor for several comments and suggestions which have resulted in a much improved presentation of our work.

Additional information

Notes on contributors

Enrique del Castillo is Distinguished Professor of Engineering in the Department of Industrial & Manufacturing Engineering at the Pennsylvania State University (PSU). He also holds an appointment as Professor of Statistics at PSU and directs the Engineering Statistics and Machine Learning Laboratory. Dr. Castillo’s research interests include engineering statistics with particular emphasis in design of experiments, time series control, and applied Bayesian statistics. An author of over 100 refereed journal papers, he is the author of the textbooks Process Optimization, a Statistical Approach (Springer, 2007), Statistical Process Adjustment for Quality Control (Wiley, 2002), and co-editor (with B.M. Colosimo) of the book Bayesian Process Monitoring, Control, and Optimization (CRC, 2006). An NSF Career and Fulbright Scholar awardee, he is a past editor in chief (2006-2009) of the Journal of Quality Technology.

Adam Meyers is a Ph.D. student in the Industrial Engineering Department at Penn State University, University Park, PA.

Peng Chen is a Ph.D. student in the Industrial Engineering Department at Penn State University, University Park, PA.