Network Analysis II

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 or have prior experience with managing network data, network visualization, and descriptive network analysis.

Dates

This one-week, 17.5-hour course runs Monday-Friday, July 10-14, 2023. The course is scheduled for 9:00 am-12:30 pm, but the timing can be adjusted so as to maximize the ability of all participants to engage during normal business hours.

Instructor

Adam D. Henry, University of Arizona

Detailed Description

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:

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 the quadratic assignment procedure (QAP) and multiple regression quadratic assignment procedure (MRQAP), 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 provides 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 I. Alternatively, participants should have prior experience with descriptive network analysis and some familiarity with the statistical software 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.