This course explores the use of social network analysis to understand the growing connectivity and complexity in the world around us on different scales-ranging from small groups to the World Wide Web. It examines how we create social, economic, and technological networks, and how these networks enable and constrain our attitudes and behavior. The course will discuss how social network concepts, theories, and visual-analytic methods are being used to map, measure, understand, and design a wide range of phenomena such as social networking sites (e.g. Facebook, Myspace), recommender systems (e.g., Amazon, Netflix, Pandora), trust and reputation systems (e.g., eBay, Epinions, Slashdot), search engines (e.g., Flickr, Wikipedia, Yelp), social bookmarking (e.g., Delicious, Digg, Reddit), and virtual worlds (e.g., Second Life, EverQuest 2, World of Warcraft). The course has no formal prerequisites but will be most beneficial to students who have had an introductory statistics course covering descriptives for central tendencies, correlation, sampling, and significance testing. If you are already familiar with bipartite networks, multigraphs, small worlds, preferential attachment, power laws, exponential random graph models, homphily, and diffusion, you may still find much to learn!
