Professor Henry is a fantastic instructor! He is genuinely interested in helping his students succeed. — participant from the U.S.
Network concepts are increasingly prevalent across a wide range of social science disciplines and are often used as a tool to study complex phenomenon, such as cooperation, diffusion of innovation, and social capital. This course is concerned with models of network evolution and advanced methods for analyzing and making statistical inferences about network structures and the behaviors of network actors. It explores major theoretical questions that motivate the study of networks, but is primarily focused on providing participants with the practical skills that allow them to conduct advanced analyses of network data and address real-world problems.
This course is the second part in a two-course sequence. It focuses on models of network evolution and inferential network analysis and requires that participants are familiar with the material covered by the introductory Network Analysis I or have prior experience with managing network data, network visualization, and descriptive network analysis.
This one-week, 20-hour course runs Monday-Friday, 9:00 am-1:00 pm, June 26-30, 2017.
After a brief review of topics related to managing network data, network visualization, and descriptive network analysis, this course explores models of network growth and evolution, applications of these models to real-world networks, and techniques used to make inferences about network structures. Inferential network analysis differs from traditional statistical inference techniques because they allow the analyst to explicitly account for the interdependence between nodes and links in a network.
The course covers the following topics:
Moreover, this course provides training in related topics and skills, including programming in R as well as the use of agent-based models to understand network evolution.
We strongly encourage participants to combine this course with the introductory Network Analysis I. Alternatively, participants should have prior experience with descriptive network analysis and some familiarity with the statistical software R.
Participants are expected to bring a WiFi-enabled laptop computer. Access to data, temporary licenses for the course software, and installation support will be provided by the Methods School.
Scott, John. 2000. Social Network Analysis: A Handbook. 2nd edition. Thousand Oaks, CA: Sage Publications.
Teetor, Paul. 2011. R Cookbook. Sebastopol, CA: O'Reilly Media.
Wasserman, Stanley, and Katherine Faust. 1994. Social Network Analysis: Methods and Applications. New York, NY: Cambridge University Press.
Henry, Adam D., and Björn Vollan. 2014. Networks and the Challenge of Sustainable Development. Annual Review of Environment and Resources 39: 583-610.