Network Analysis 1

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

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. We will explore four major questions that motivate the study of networks in political science and public policy:

How do different network structures influence our ability to solve complex policy problems?
How are real-world networks structured?
How do networks self-organize and evolve over time?
What types of interventions can lead to more effective network structures?

Although the study of networks is theoretically motivated, we are 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 of a two-part sequence (with Network Analysis 2). The first part (Network Analysis 1) is concerned with managing network data, visualization of networks, and descriptive network analysis. The second part (Network Analysis 2) is concerned with models of network evolution and methods for making statistical inferences about network structures and the behaviors of network actors.

Part 1 and Part 2 of this course can be taken independently of each other. ie. Part 1 can be taken without taking Part 2, and Part 2 can be taken without taking Part 1.

Dates

This one-week, 17.5-hour course runs Monday-Friday, 30 June - 4 July, 2025. The course is scheduled for 9.00am-12.30pm.

Classroom Location

Faculty of Arts and Social Science

Instructor

Adam Douglas Henry, University of Arizona

Detailed Description

In Network Analysis 1, we focus primarily on developing the basic skills of working with network data, visualization, and running descriptive analyses of network data. Descriptive network analysis refers to the measurement of positions that actors have in a system (i.e., who is relatively central, and who is relatively peripheral?), the identification of cohesive subgroups (i.e., what distinct communities exist within a larger structure?), as well as the characteristics of entire network structures (i.e., is a system relatively fragmented or integrated?).

Using real-world datasets drawn mainly from prior research studies on environmental policy, 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 sciences, such as social capital and brokerage.

Moreover, this course will provide training in related topics and skills, including:

How to use R and contributed packages for network research as well as other types of analysis,
How to create and interpret network graphics, and
How to empirically measure network structures.

Prerequisites

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.

Requirements

Participants are expected to have access to an internet-connected 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. 2^nd edition. Thousand Oaks, CA: Sage Publications.