Introduction to Social Network Analysis

Social network analysis studies the patterned social relationships linking social entities, such as people, organizations, and countries. It seeks to understand how interdependence and social context shape the behavior of these entities. This course will present methods for analyzing network data and show how to implement them using the R programming language. We will also see how network concepts relate to social science theory about small groups, organizations, social capital, etc. The first part of the course emphasizes descriptive network analysis, covering basic statistics for describing networks, approaches to visualizing network data, measures of the centrality of individual units within a network, and methods for detecting network subgroups. The latter part considers inferential analyses that accommodate network data's complex structure of interdependencies. Models studied here include exponential random graph models (ERGMs), network autocorrelation models, and stochastic actor-oriented models (SAOMs).