UC Irvine

Statistical network models have been shown to be of particular relevance for understanding various phenomena; one of the richest areas for research is understanding the spread of sexually transmitted infections such as HIV. With the advent of new epidemiological protocols designed to prevent spread and foundational work analyzing the impact of network structure on diffusion of contagion, network analysis is poised to tackle various questions relating to the spread of HIV amongst vulnerable populations. Chapter 1 presents a methodological development integrating Goffman's conception of stigma within the exponential random graph modeling framework. Various properties are explored under simulation and as a test case, this is used to quantify the level of behavioral stigma in an adolescent friendship network based on gender. Chapter 2 utilizes the development from Chapter 1 to quantify the level of HIV stigma in informal social networks of young black men who have sex with men (from a structural perspective). This is then linked to a framework for understanding the consequences of network perturbations in the resultant network structure (i.e. the impacts of "coming out HIV positive" on the network) and its subsequent effects on HIV diffusion. Chapter 3 focuses on issues of data collection in informal social networks, specifically resolving conflicting self-reports on relationships within the context of informant accuracy and network inference. The tools developed in this dissertation are far-reaching and can provide insight to the study of populations at risk or other social environments beyond HIV.