Applied Social Network Analysis - Online Course
An 8-Hour Livestream Seminar Taught by
Craig M. Rawlings10:30am-12:30pm (convert to your local time)
1:00pm-3:00pm
The focus of this seminar is the application of social network methods to the empirical analysis of real-world data sets. Rather than discussing the basics of social network analysis (SNA), we will focus instead on the “now what?” question that comes after having obtained social network data. In short, we will get to work on the various ways of describing and analyzing social structures. The emphasis is on getting lots of results that can eventually be curated and refined for a research report.
We will briefly review the big picture of why social network analysis is so useful for social scientists, especially for understanding core theoretical concerns such as modeling diffusion, social influence, and integrated social structures (i.e., roles and communities). The bulk of the course will then be aimed at providing the techniques needed to “see” social structures using visualizations and various metrics. Following this course, you should be more prepared to take an advanced course on the statistical modeling of social networks (such as Social Networks: Statistical Approaches).
Detailed sets of tutorials in R will be provided. Each tutorial will walk you through the logic of social network tools using real-world social network data, with the goal of developing the tools needed to understand where these social structures come from and how they affect the social world.
Starting December 12, this seminar will be presented as an 8-hour synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 30-minute break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
The course will draw on the instructor’s recent coauthored book Network Analysis: Integrating Social Network Theory, Method, and Application with R. The book has a large set of accompanying detailed R tutorials.
Rather than try to cover the entire book, we will focus on the chapters and tutorials that get at the key results that help the researcher understand social structural features in their networks. The course does not emphasize theory or math, but rather how to solve the practical problems that arise when actually analyzing network data. The emphasis will be on generating the visualizations and analyses that are the core endeavor of seeing the structure of a network.
On Day 1, we will introduce the basics of seeing structure. The guiding question of the day will be: “How cohesive is my network?” We will answer this question using basic network visualizations, dyad/triad censuses, and centrality/centralization measures. We will delve deeper into the question by analyzing social structure as more or less cohesive and integrated communities, as well as role positions.
On Day 2, we will introduce the basics of structural prediction. The guiding question of the day will be: “What are some of the causes and consequences of such a structure?” We will focus on the exogenous and endogenous factors that generally combine in forming social structures, and then some ways to think about structures as conduits for diffusion and social influence. We will close with a discussion of two-mode networks and duality in network structures.
The course will draw on the instructor’s recent coauthored book Network Analysis: Integrating Social Network Theory, Method, and Application with R. The book has a large set of accompanying detailed R tutorials.
Rather than try to cover the entire book, we will focus on the chapters and tutorials that get at the key results that help the researcher understand social structural features in their networks. The course does not emphasize theory or math, but rather how to solve the practical problems that arise when actually analyzing network data. The emphasis will be on generating the visualizations and analyses that are the core endeavor of seeing the structure of a network.
On Day 1, we will introduce the basics of seeing structure. The guiding question of the day will be: “How cohesive is my network?” We will answer this question using basic network visualizations, dyad/triad censuses, and centrality/centralization measures. We will delve deeper into the question by analyzing social structure as more or less cohesive and integrated communities, as well as role positions.
On Day 2, we will introduce the basics of structural prediction. The guiding question of the day will be: “What are some of the causes and consequences of such a structure?” We will focus on the exogenous and endogenous factors that generally combine in forming social structures, and then some ways to think about structures as conduits for diffusion and social influence. We will close with a discussion of two-mode networks and duality in network structures.
Computing
To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R installed. You are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
An introductory understanding of R is helpful. However, prior to the beginning of the course, tutorials on the basics of R will be provided.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R installed. You are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
An introductory understanding of R is helpful. However, prior to the beginning of the course, tutorials on the basics of R will be provided.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should register?
This course would be most helpful for those who have a basic understanding of SNA, such as the one offered in an introductory course, as well as a basic understanding of statistical modeling. However, neither is required. This course is ideal for those who want a deeper dive into the practical skills needed to understand messy real-world data and produce meaningful, interpretable results.
Some basic familiarity with R will be required for completing the tutorials. However, additional tutorials will be given before the beginning of the seminar that will get even the novice ready for the course.
This course would be most helpful for those who have a basic understanding of SNA, such as the one offered in an introductory course, as well as a basic understanding of statistical modeling. However, neither is required. This course is ideal for those who want a deeper dive into the practical skills needed to understand messy real-world data and produce meaningful, interpretable results.
Some basic familiarity with R will be required for completing the tutorials. However, additional tutorials will be given before the beginning of the seminar that will get even the novice ready for the course.
Seminar outline
Day 1: “How Cohesive is My Network Structure?”
- Why structure matters
- Network visualization
- Dyad/triad censuses
- Communities, role positions
Day 2: “What are the Causes/Consequences of this Network Structure?”
- Exogenous factors
- Endogenous factors
- Diffusion & influence
- Duality
Day 1: “How Cohesive is My Network Structure?”
- Why structure matters
- Network visualization
- Dyad/triad censuses
- Communities, role positions
Day 2: “What are the Causes/Consequences of this Network Structure?”
- Exogenous factors
- Endogenous factors
- Diffusion & influence
- Duality
Payment information
The fee of $695 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $695 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.