Network Analysis

Prof. Henry did a fantastic job at catering to the needs of a diverse group of participants. The whole course was excellent! — participant from Russia

This course is concerned with methods commonly used to analyze networks in the social sciences. 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 provides them with the analytical tools to understand and address real-world problems. Participants learn, through hands-on training in the free software package R, how to manage network data and perform essential descriptive and inferential analyses on these data.


This course was offered in 2016.


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. We will examine three 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 practical skills in working with network data and performing basic network analyses. Participants learn, through hand-on training in R and actual network datasets, how to manage network data and how to perform essential descriptive and inferential analyses using these data.

Descriptive network analysis techniques focus on measuring characteristics of entire networks as well as positions of individual actors (or 'nodes') within a network. Participants will learn concepts of network centrality, clustering, community structure, and network segregation, and how these concepts may be used to capture variables of theoretical interest in the social science, such as social capital and brokerage.

Participants also gain an introduction to inferential techniques in network analysis, which differ 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 introduces participants to the quadratic assignment procedure (QAP), multiple regression quadratic assignment procedure (MRQAP), and exponential random graph models (ERGM).


There are no formal prerequisites. It would be beneficial if participants had some experience with R. However, even participants unfamiliar with this statistical software and programming language 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

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

Wasserman, Stanley, and Katherine Faust. 1994. Social Network Analysis: Methods and Applications. New York, NY: Cambridge University Press.

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