The purpose of this graduate-level seminar is to introduce students to the study of social networks. It will provide students with an overview of basic concepts used in the literature of social networks, their theoretical underpinnings, and applications in sociology. We will read through old and new literature that evolves around how individuals interact with one another, how structure emerges out of interaction, and how network structures influence outcomes at both the individual as well as macro level. Even though we might walk through a bit of mathematical formalization on selected topics (at a rudimentary level), this is \emph{not} a course on methodology. This means that the course will not deal with data analysis and that there will be no coding exercises. Also, while the course has no formal prerequisites, it is assumed that students have a basic understanding of how quantitative methods are used in the social sciences. Most materials should be accessible to students with an introductory background in graduate-level social statistics and a readiness to learn a bit of matrix algebra.

All notes below are intended to help students with their understanding of these technical issues. But, except for the note on random walks, they are optional material for the curious students, rather than being required. Pointing out any typos or errors in these materials is greatly appreciated.

Notes

• The Friendship Paradox and Its Generalization
• Random Graph and DiPrete et al.’s (2011) Model of Network Degree
• Eigenvectors and Random Walks on Networks
• Cosine Similarity
• Modularity and the Map Equation (Slides)