September 16, 2022
9 a.m. – 5 p.m.
Hosted by the Center for Social & Behavioral Science.
During the workshop, we reviewed the foundations of network analysis starting from data collection through modeling outcomes and how to think about data, measures, and models. The workshop concluded with a discussion of additional methods and a Q&A. If you were unable to join us for the workshop or if you would like to revisit some of the presented content, recordings for each session of the workshop are now available:
More Information about the workshop
Social network analysis focuses on how to measure and model relations within a population and how such relations affect behavior. Networks have been applied across a wide array of social scientific questions, including peer influence, social capital, hierarchy, disease diffusion, terrorism and world-systems models to name just a few. The core insight of network models is that social actors are not independent: behavior is shaped by the rich pattern of associations one is embedded within. Breaking with the common statistical independence assumption requires different ways of conceptualizing data and best characterizing complex relational patterns requires specialized tools and approaches. In this workshop we will review the foundations of network analysis starting from data collection through modeling outcomes. The workshop will cover how to think about data, measures, and models. No coding or prior experience is necessary; class examples will be demonstrated mainly within the R computing language (with some pointers to alternatives). Example code will be available for those interested in running the models themselves.
For those interested in a brief introduction, we recommend:
- Chapters 1 and 2 of the Oxford Handbook of Social Networks (Light & Moody, 2020)
- John Scott, Social Network Analysis, Sage University Press (4th ed now out)
- Small, Perry, Pescosolido & Smith, 2022. Personal Networks: Classic Readings and New Directions in Egocentric Analysis. Cambridge U Press.
About Prof. James Moody
James Moody is a Professor of Sociology at Duke University. He has published extensively in the field of social networks, methods, and social theory. His work has focused theoretically on the network foundations of social cohesion and diffusion, with a particular emphasis on building tools and methods for understanding dynamic social networks. He has used network models to help understand school racial segregation, adolescent health, disease spread, economic development, and the development of scientific disciplines. Moody’s work is funded by the National Science Foundation, the National Institutes of Health and the Robert Wood Johnson Foundation and has appeared in top social science, health and medical journals. He is winner of INSNA’s (International Network for Social Network Analysis) Freeman Award for scholarly contributions to network analysis, founding director of the Duke Network Analysis Center and former editor of the on-line Journal of Social Structure. For more information, see https://scholars.duke.edu/person/james.moody.