Social Network Analysis

Sociology 543
Princeton University
Spring 2017

Monday 1:30pm-4:30pm
006 Wallace Hall
Instructor: Matthew Salganik


This 12 week graduate seminar will provide students an introduction to social network analysis. This course will approaches networks as an orienting perspective, as a set of methods, and as a topic of empirical investigation. Because the study of networks is interdisciplinary, we read materials from many different fields. Students from other departments are welcome, and I will assume statistical knowledge equivalent to what is provided in Soc 500/Soc 504.

Course goals and learning objectives

  1. Students will be able to describe the major research strands in the study of networks, as well as the connections between these strands.
  2. Students will be able to evaluate modern research that advances the study of networks or uses network ideas to advance the study of some other topic.
  3. Students will be able to create research proposals that potentially advance the study of networks or use network ideas to advance the study of some other topic.
  4. Students will create new research that actually advances the study of networks or uses network ideas to advance the study of some other topic.

Course activities

Meeting structure

Each class meeting will be split into four main parts:

In general, the class will be a mix of professor-led discussion and student-led discussion. As the semester progresses, I will expect the students to take an increasingly active role in the course.


See the logistics page for more information about time and location, reading expectations, collaboration policy, Piazza, grading, and open access.

Introduction and the small-world problem (February 6, 2016)

In this first class we will start with some introductory reading and then learn about the work done on the so-called "small-world" problem. This problem is a nice way to begin the course because it touches on many themes we will revise throughout, and it is one of the few problems in network analysis that has had a sustained combination of empirical and theoretic work.

Network structure, more global measures (February 13, 2016)

What does a given network look like? For the next two weeks, we will review common measures of network structure at both the global level and the local level. We will also focus on the connection between these two, and we will read examples of these ideas being applied to empirical research in a variety of domains.

Network structure, local measures (February 20, 2017)

This week will focus on more local measure of network structure, and particularly how these local patterns can aggregate to produce global structures. We also read several examples of empirical work triggered by some of this theoretical work.

Diffusion, spread, and contagion: models (February 27, 2017)

This week we will review a number of models of how things---both diseases and social behavior---spread and how that spread is affected by the structure of the underlying contact network.

Diffusion, spread, and contagion: experiments and empirics (March 6, 2017)

This week we will review attempts to empirically study the spreading of social behavior on networks and the many of the challenges involved. Both experimental and observation studies will be discussed.

Filter bubbles, echo chambers, and the spiral of silence (March 13, 2017)

This week we will study the related concepts of filter bubbles, echo chambers, and the spiral of silence.

Respondent-driven sampling (March 27, 2017)

Respondent-driven sampling is a network-based technique for studying hard-to-reach populations that is now being used around the world.

Network interventions (April 3, 2017)

Often researchers or policy makers which to intervene in a network in order to create a certain outcome such as decreased bullying or adoption of better health practices. This week will consider emperical and theoretical work address these issues. We will also have a special guest -- Betsy Paluck -- to talk about some of her work in progress.

Network scale-up method (April 10, 2017)

The network scale-up method is a network-based technique for studying hard-to-reach populations that is now being used around the world.

Networks and time (April 17, 2017)

Given the growing availability of "digital trace" data, we now have the ability to study how networks change in time, but this also introduces a number of conceptual questions. What if the data we have is not about ties, but about interactions (e.g., email exchanges, conversations, sexual encounters)? What does the dynamics of ties mean for the spreading processes we read about previously? This week we will review some of has been done in this emerging area of research.

Face-to-face contact and the spread of disease (April 24, 2017)

Project presentations (May 1, 2017)

For the final week of class, students will present their projects and get feedback. I will post more on the exact format later in the semester.


This class was shaped by conversations with Brandon Stewart, especially his class on Text as Data from Spring 2016.

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