Hosted by the GPH Department of Biostatistics and the NYU Grossman Department of Population Health Division of Biostatistics
While social network data provide new opportunities to understand complex relational mechanisms, they also present modeling challenges. Units of observation in social network are often not independent and identically distributed, as commonly assumed in many statistical models, and hence require new tools to analyze the data, to make inference and address issues of model selection and goodness of fit, while accounting for the complex dependence structures. Many recent developments have been made in statistical methodologies to account for such complications. In particular, latent variable network models that accommodate edge correlations implicitly, by assuming an underlying latent factor, are increasing in popularity. Although, these models are examples of what is a growing body of research, much of the research is focused on proposing new models or extending others. There has been very little work on unifying the models in a single framework.
In this talk, Dr. Samrachana Adhikari will first review different latent variable network models for analyzing social network data. He will then introduce a complete framework that organizes existing latent variable network models within an integrative generalized additive model, called the Conditionally Independent Dyad (CID) models. The class of CID models includes existing network models that assume dyad (or edge) independence conditional on latent variables and other components in the model. By presenting analysis of advice seeking network of teachers as an example, he'll illustrate the utility of the proposed framework. The conclusion will discuss existing and future extensions of the proposed class of network models to incorporate multiple related networks.
About the Speaker
Dr. Samrachana Adhikari is an Assistant Professor of Biostatistics in the Department of Population Health, NYU School of Medicine. Her research interests lie in developing and implementing statistical and machine learning tools to solve problems motivated by real-world applications in medicine, global health, and education.
In-person attendance is only available to NYU students, faculty, and staff. A live virtual option is available for the general public. Please register to confirm your mode of attendance. For questions, contact Evan Wardell at evan.wardell@nyu.edu.