The overall goal of the course is to provide students with a working knowledge of the basic concepts and measures used to describe and analyze social networks and the ability to understand the results and implications of this body of research. Social network analysis applies graph theory to characterize relationships between networked entities (i.e., people, groups, places, organizations, and/or other units). Social network analysis is used to understand how these relations influence attitudes, beliefs, behaviors; diffusion of ideas and behaviors; the spread of infectious diseases; human mobility patterns; communication and collaborative networks; social media networks; and biological systems, among other things. Although social network analysis has a long history of use in sociology, it is also commonly used in many other scientific disciplines, including anthropology, business, communication, computer science, economics, education, marketing, medicine, public health, political science, psychology, and many others. Within public health, network analysis is commonly applied to the study of social media networks, friendship networks among adolescents, citation and co-authorship networks, road networks, disease transmission networks, risk potential networks (i.e., networks with the potential for disease transmission), networks of gangs or terrorists, global trade networks, genome-wide association networks, phylogenetic networks (i.e., putatively linked infections based on viral sequence similarities), etc. For example, social network analysis can be used to understand the epidemiology of infectious diseases, inform vaccination and quarantine strategies during outbreaks, develop peer-driven interventions to change behavioral norms, employ the use of peer health navigators to find individuals who have dropped out of care and connect them with services, identify new infections via contact tracing, study diffusion of innovations, inform organizational improvements, or develop strategies to provide more coordinated health care services. This course consists of class lectures, group discussions, student presentations, labs, reading materials, and problem sets. Of note, problem sets will include additional higher-level thinking questions which will be mandatory for PhD students and optional for all others taking the class. Data analyses will be conducted using Stata, UCINET and Netdraw software packages.