Social Network Analysis

Aim

After completing the course the student should at an advanced level be able to:

• explain basic concepts and theories of network analysis in the social sciences, and understand how these concepts and theories can help explain different actors’ micro behaviors as well as macro outcomes;
• critically examine the ways in which networks can contribute to the explanation of social, political, economic and cultural phenomena;
• use statistical software to visualize networks and analyze their properties, connecting these to network concepts and theories;
• explain principles underlying statistical models for social networks;
• use software to implement statistical models of social networks to analyze network formation and evolution;
• use software to simulate the dynamics of networks based on social network models.

Content

This course presents key concepts, measures, and statistical techniques needed for the analysis of relational, social network data using a computational approach. Network concepts such as centrality and brokerage are discussed, and popular measures related to these concepts are reviewed. The course proceeds to computational methods for handling network data, producing network visualizations, and calculating relevant statistics. Statistical models applicable to network data are considered, and tutorials in relevant software tools are provided. Various statistical models for network data are presented and estimated in interactive computer labs involving real data, and methods for simulating network models are implemented.