UCLA

This dissertation considers credible models for social network data. In the classical socialnetwork analysis setting, nodes are connected to each other with the connections between the nodes stochastic and interdependent. As a result social network modeling has a sample size of one, where each network observed must be regarded as a single realization of a stochastic process. I consider three distinct but related topics in this field in each of the chapters.

I first consider the Latent Order Logistic (LOLOG) model, a recently proposed model classfor social network modeling. I take the data centric viewpoint, that is, how does the LOLOG model perform in terms of fit, qualitative interpretation and scientific conclusions on data that practitioners fit with commonly used exponential-family random graph models (ERGM).

I also propose a method for Bayesian inference for the LOLOG model. This method alsoyields insight into the posterior distribution of the latent edge ordering, which is fundamental to the LOLOG model. This approach allows for deeper insight into the LOLOG process, as well as insight into the reasons for LOLOG’s desirable properties for social network analysis.

The final chapter of the thesis considers a separate topic. I consider an approach for causalinference with observational data in a network setting. Current approaches assume the network is observed and exogenous. The main contribution of this chapter is to regard the edges and nodal covariates as jointly distributed in a causal model. We consider the causal structure of this problem, and provide an extension to current approaches to account for this . Throughout this dissertation, I use real-data networks to demonstrate the ideas and methodology. Examples include the National Longitudinal Study of Adolescent to Adult Health and a well known network of New England Lawyers.