Social Network Analysis: An Introduction with an Emphasis on Application in R (Annapolis, MD)


Social network analysis studies the relationships between or among social entities. While network science has a long tradition, this field has recently exploded with new data resources in social media and new computational methods. This course presents an introduction to various concepts, methods, and applications of social network analysis drawn from the social and behavioral sciences. Topics include basic network descriptive measures: structural and locational properties of actors (centrality, prestige, and prominence); structural cohesion (subgroups and cliques); equivalence of actors (structural equivalence and blockmodels); local analyses (dyadic and triadic analysis); methods for testing hypotheses about social network structure (matrix permutation tests, conditional uniform random graphs); and a brief introduction to statistical global analyses (p1, p*, ERGMs, and their relatives). The focus of the course will be how to develop questions about social networks and appropriately test them. We will use the R statistical programming language.

Prerequisites: No prior experience with R is required; by the end of the course you will be able to write simple programs for your own social network analysis needs. Participants are encouraged to bring their own network data to work with, but this is not required.

Fee: Members = $1700; Non-members = $3200

Tags: social Network, R

Course Sections