LIS 505L Introduction to Social Network Analysis
This course intends to give students a general introduction to doing social network analysis. The focus is on methodology, from data collection to data analysis (using UCINET software). Since methodology and theory is inseparable, this course is also about social network theory. What this course is not is a review of mathematics graph theory and computer programming on network analysis. Networks can be found everywhere. Relatives are a kind of a network connected by blood relationship with each other. In schools and companies, people are interconnected with all the way through a variety of other people living in the network. Not only is the relationship between people represented by the networks. Subway map is a network of subway stations. Interlibrary loan services are a network of libraries. Social network sites connections are a network of cyberspace. Citation map is a network of documents, institutions, nations, or patents. The spread of avian flu genes can be represented as a network that is based on genetic link between each other. The fact itself that so many things in the world can be represented as a network is simply a good reason to explore and analyze networks. With the rise of the network view, social network analysis is gaining attention in researchers, journalists, and the general public. It becomes increasingly popular in library and information science, physics, medical science, biology as well as in sociology, economics, and business administration. Network analysis helps better understanding the interdependence between the network components and provides an important solution for improving the efficiency and effectiveness of the entire network. Regarding the Silicon Valley innovations, the social network view focuses on the employee connections and communication networks across organizations while the traditional view is focusing on an individual employee's high educational level and expertise. This course will present an introduction to various methods, concepts, and applications of social network analysis drawn from the social and behavioral sciences. Topics to be discussed include a basic introduction to social networks, graphs and matrices, social network data collection, degree and density, centrality and power, structural holes and broker, cohesive subgroup, position and role, and visualization of social networks.