Collaborative Research: Modeling Unobserved Heterogeneity in Network Formation

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General SummaryThe statistical analysis of networks has become increasingly important in the social and behavioral sciences and has been applied to a diverse range of problems in recent years. Indeed, the National Science Foundation's agenda setting statement for the social sciences, "Rebuilding the Mosaic," recognizes Network Science as one of four critical research frontiers. Yet, while the statistical analysis of networks continues to attract a great deal of attention from scholars and the broader public, the quantitative study of networks remains in the early stages of development. Only recently have the statistical theory and computational techniques been developed to rigorously analyze various types of networks. The central aim of the project is to develop a new model for identifying statistical effects on network formation that explicitly accounts for unobserved variation. Technical SummaryThe PIs develop an estimator that accurately captures unobserved heterogeneity in tie formation in network models. They do so by extending the widely applied Exponential Random Graph Model (ERGM) to include a frailty term that accounts for unmeasured, unobserved, or unimagined heterogeneity. Unaccounted heterogeneity is a significant issue in the study of social processes; thus, the inability to effectively model it is an important gap that limits the applicability of ERGMs in many areas of potential interest. In fact, one of the two major assumptions of the ERGM is that the model is correctly specified, and coefficient bias or model degeneracy may result from violations of this assumption. The PIs propose to extend the ERGM to account for this problem through the introduction of a frailty term, thereby creating a Frailty Exponential Random Graph Model (FERGM). In addition to defining the FERGM and providing Monte Carlo simulations to demonstrate the model properties and comparative benefits of the approach, the PIs apply the FERGM to substantive topics in the social and health sciences and provide related statistical software for others to do so as well. Advances in network modeling will be useful across scientific disciplines, including sociology, economics, statistics, computer science and behavioral health. The focus on social science applications will aid practitioners in public policy by allowing them to more accurately evaluate the importance of policy on economic and social outcomes. Finally, the project directly promotes teaching, training, and learning and broadens participation of underrepresented groups in scholarly activity.