In the study of social processes, the presence of unobserved heterogeneity is a regular concern. It should be particularly worrisome for the statistical analysis of networks, given the complex dependencies that shape network formation combined with the restrictive assumptions of related models. In this paper, we demonstrate the importance of explicitly accounting for unobserved heterogeneity in exponential random graph models (ERGM) with a Monte Carlo analysis and two applications that have played an important role in the networks literature. Overall, these analyses show that failing to account for unobserved heterogeneity can have a significant impact on inferences about network formation. The proposed frailty extension to the ERGM (FERGM) generally outperforms the ERGM in these cases, and does so by relatively large margins. Moreover, our novel multilevel estimation strategy has the advantage of avoiding the problem of degeneration that plagues the standard MCMC-MLE approach.
More About Dr. Jason Morgan
Equipped with his robust and rich academic background, Jason Morgan fearlessly leads Wiretap's data science team every day.
In addition to solving large enterprise problems, Jason is a true educator and academic. He has taught courses in network modeling and statistical methodology at the undergraduate and graduate level and is currently co-authoring a textbook on social network models titled Inferential Network Analysis, which is under contract with Cambridge University Press. Additionally, Jason has contributed to notable academic journals such as, Annals of Applied Statistics and Political Analysis, and presented at top national conferences, including Black Hat.
Learn more about his work at Wiretap in his recent presentation at Techstars Startup Week Columbus on building a data science team at a start-up: