Network Analysis I   

Prof. Henry was always willing to go the extra mile and explain everything very clearly. His course provides an excellent introduction to network analysis. I learned a lot! — participant from Hong Kong

Network concepts are increasingly prevalent across a wide range of disciplines and are often used as a tool to study complex phenomenon, such as cooperation, diffusion of innovation, and social capital. This course introduces participants to major research questions in the study of networks and is concerned with basic methods commonly used to analyze networks in the social sciences. It covers such topics as the managing of network data, visualization of networks, and descriptive network analysis. Although the study of networks is theoretically motivated, this course is primarily focused on developing practical skills in working with network data. Participants learn, through hands-on training in the free software package R, how to manage and rigorously analyze network data.

This course is the first part in a two-course sequence. Part two (cf. Network Analysis II) is concerned with models of network evolution and advanced methods for drawing statistical inferences about network structures and the behaviors of network actors.


This one-week, 17.5-hour course runs Monday-Friday, 9:00 am-12:30 pm, July 1-5, 2019.


Adam D. Henry (picture), University of Arizona

Detailed Description

This course begins with a theoretical examination of networks and their importance in the social sciences and discussion of essential research questions that motivate the study of networks, including:

  • How do various types of networks influence social, political, and economic problems of interest?

  • How do networks self-organize and evolve over time?

  • What contextual or institutional factors influence how networks evolve and influence problems of interest?

Although the course is theoretically motivated, it primarily focuses on developing the basic skills of working with network data, network visualization, and running descriptive analyses of network data. It covers descriptive network analysis techniques that allow participants to measure the positions of individual actors (or 'nodes) within a system, identify cohesive subgroups, as well as analyze characteristics of entire network structures. Participants learn about concepts of network centrality, clustering, community structure, and network segregation, and how these concepts can be used to capture variables of theoretical interest in the social science, such as social capital and brokerage.

Through hand-on training in R and the use of real-world datasets, participants acquire practical skills that allow them to create and interpret network graphics, empirically measure network structures, and answer such questions as who is relatively central, who is relatively peripheral, what distinct communities exist within a larger structure, or is a system relatively fragmented or integrated?


There are no formal prerequisites. It would be beneficial if participants had some experience with R. However, even participants unfamiliar with this statistical software will be able to effectively participate in the course.


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.

Core Readings

Scott, John. 2000. Social Network Analysis: A Handbook. 2nd edition. Thousand Oaks, CA: Sage Publications.

Suggested Readings

Teetor, Paul. 2011. R Cookbook. Sebastopol, CA: O'Reilly Media.

Henry, Adam D., and Björn Vollan. 2014. Networks and the Challenge of Sustainable Development. Annual Review of Environment and Resources 39: 583-610.

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