Network Analysis 2

Adam is an excellent instructor, and he knew his stuff a lot and provided us with a lot of insightful analysis regarding how we could apply and study network analysis. — participant from South Korea

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 second of a two-part sequence (with Network Analysis 1). 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, 7 - 11 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 2, we build on the topics and skills developed in Network Analysis 1 to the statistical analysis of networks. After a brief review of topics from Network Analysis 1, we will explore 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. This course will explore the following topics:

Random graph models and the application of random graphs to understand how real-world networks grow and evolve over time,
How to quantify the similarity and differences between networks using quadratic assignment procedure (QAP), and
How to estimate the processes that generate networks, such as homophily (attraction to those who are similar) and transitivity (where friends of friends become friends) using exponential random graph models (ERGM).

Moreover, this course will provide training in related topics and skills, including programming in R as well as the use of agent-based models to understand network evolution.

Prerequisites

We strongly encourage participants to combine this course with the introductory Network Analysis 1 course. Participants should have a familiarity with descriptive network analysis and a basic working knowledge of R.

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.