| Tutorial
Most people think of social networks as being comprised of friends, family, and co-workers. However, social networks can be found anywhere people interact. In fact, social networks are a type of complex system. Researchers who study complex systems have found that the patterns in social networks can provide valuable insights into human behavior.
One tool that researchers use to study social networks is called social network analysis (SNA). SNA is a quantitative method for analyzing relationships between people or organizations. SNA can be used to answer questions such as: Who is connected to whom? How are they connected? What are the patterns in the connections?
Researchers who use SNA typically collect data on the relationships between people or organizations. This data can be collected through surveys or observations. Once the data is collected, it is entered into a computer program that produces graphs and other visual representations of the data. These visualizations can then be analyzed to identify patterns and trends in the data.
SNA is a valuable tool for understanding human behavior because it can help identify how information and ideas flow through a social network. For example, if you wanted to understand how a new product would spread through a population, you could use SNA to map out the social network of the people who are likely to adopt the product. This would allow you to identify which individuals are most likely to influence others and help spread information about the product.
There are many different software programs that can be used for SNA. Some of these programs are free and open-source, while others are commercial software packages that must be purchased. R is one example of a free and open-source software program that can be used for SNA. R is a statistical programming language that is popular among statisticians and data scientists. R has many built-in functions for performing statistical analyses, including functions for performing SNA.
In this tutorial, we will show you how to use R to perform SNA on a small dataset. The dataset we will use contains information on relationships between members of Congress from the 113th Congress (2013-2014). The dataset was collected by Michael Kousen and Tom Kevenaar and was originally published in their book Social Network Analysis with R (Kevenaar & Kousen, 2015). The dataset contains information on co-sponsorship of bills, committee memberships, and voting patterns in Congress. We will use this dataset to answer some basic questions about the structure of relationships between members of Congress.
