Social Network Analysis
006 Wallace Hall
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
- Students will be able to describe the major research strands in the study of networks, as well as the connections between these strands.
- 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.
- 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.
- Students will create new research that actually advances the study of networks or uses network ideas to advance the study of some other topic.
- Shorter research proposal (x3): During the semester, you will write 3 short research proposals. These proposals are described in more detail in the research proposal guidelines. These proposals can be done in teams, and I encourage you to work on your proposal with someone from a different department. This is a lot of proposals, but the best way to have a good idea is to have lots of ideas.
- Proposal review (x11): For weeks 2 to 11, you will write a review of the proposals of your peers. These proposal reviews are described in more detail in the research proposal review guidelines. The goal of your proposal review is to be as helpful as possible.
- Research project (x1): The final project is due May 16, 2017 at 5pm. You are encouraged to work in groups for this project, especially if these groups involve people from different departments.
Each class meeting will be split into four main parts:
- Overview: I'll begin each class describing the main themes of the week.
- Papers: We will discuss the papers you have read.
- Discussion of proposals: Students will take turns presenting their proposals, and there will be a student-led discussion of the proposals.
- Preview: At the end of class, I'll preview some themes that you'll encounter in the reading for next class.
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.
- Newman, M.E.J. (2010). Networks: An Introduction (Chapter 1) [available from blackboard].
- Easley, D. and Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Chapter 1) [available from blackboard].
- Milgram, S. (1967). The small world problem. Psychology Today, 1:62-67 [Available from blackboard].
- Travers, J. and Milgram, S. (1969). An experimental study of the small world problem. Sociometry.
- Watts, D.J. and Strogatz, S.H. (1998). Collective dynamics of 'small-world' networks. Nature.
- Victor, B. (2011). Scientific Communication As Sequential Art.
- Watts, D.J. (1999). Networks, dynamics, and the small world phenomenon. American Journal of Sociology.
- Kleinberg, J. (2000). Navigation in a small world. Nature.
- Watts, D.J., Dodds, P.S., and Newman, M.E.J. (2002). Identity and search in social networks. Science.
- Kleinfeld, J.S. (2002). The small world problem. Society [Available from blackboard].
- Granovetter, M. (2003). Ignorance, knowledge, and outcomes in a small world. Science.
- Dodds, P.S., Muhamad, R., and Watts, D.J. (2003). An experimental study of search in a global social networks. Science.
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.
- Butts, C.T. (2009). Revisiting the Foundations of Network Analysis Science.
- Newman, M.E.J. (2010). Networks: An Introduction (Chapter 12.0 - 12.5 and 12.8 [Random Graphs]) [available from blackboard] (SKIM).
- Erdos-Reyni random graph animation
- Barabasi, A.L. and Albert, R. (1999). The emergence of scaling in random networks. Science.
- Barabasi-Albert random graph animation
- Fosdick et al (2016) Configuring Random Graph Models with Fixed Degree Sequences. Working paper.
- Newman, M.E.J., and Girvan, M. (2004). Finding and evaluating community structure in networks. Phys. Rev. E.
- Freeman, L.C. (2005). Visualizing Social Networks. Journal of Social Structure.
- Robins, G., Pattison, P., Kalisha, Y., and Lushea, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks 29(2)173-191.
- Baldassarri, D. and Diani, M. (2007). The integrative power of civic networks. American Journal of Sociology, 113(3):735-780.
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.
- Easley, D. and Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Chapter 21.0-21.6 [Epidemics]) [available from blackboard].
- Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology.
- Watts, D.J. (2002). A simple model of global cascades on random networks. PNAS.
- Watts, D.J. and Dodds, P.S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research.
- Centola, D. and Macy, M.W. (2007). Complex contagion and the weakness of long ties. American Journal of Sociology.
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.
- Noelle-Neumann, E. (1984) The Spiral of Silence p 1-22 (Chapter 1 and half of Chapter 2)
- Kitts, J.A. (2003). Egocentric Bias or Information Management? Selective Disclosure and Social Roots of Norm Misperception Social Psychology Quarterly 66(3): 222-237.
- Quealy, K. (2017) We Avoid News We Don’t Like. Some Trump-Era Evidence. The New York Times.
- Gentzkow, M. and Shapiro, J. M. (2011) Ideological Segregation Online and Offline. Quarterly Journal of Economics 126(4):1799-1839.
- Flaxman, S., Goel, S., Rao, J.M. (2016) Filter Bubbles, Echo Chambers, and Online News Consumption. Public Opinion Quarterly 80(S1): 298-320.
- Lazer, D. (2015) The rise of the social algorithm. Science.
- Bakshy, E., Messing, S., and Adamic, L.A. (2015) Exposure to ideologically diverse news and opinion on Facebook. Science.
- Sandvig, C. (2015) The Facebook ‘it’s not our fault’ study. Social Media Collective Research Blog.
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.
- Heckathorn (1997) Respondent-Driven Sampling: A New Approach to the Study of Hidden Populations. Social Problems. (skim)
- Heckathorn (2002) Respondent-Driven Sampling II: Deriving Valid Population Estimates from Chain-Referral Samples of Hidden Populations. Social Problems. (skim)
- Salganik and Heckathorn (2004) Sampling and estimation in hidden populations using respondent-driven sampling. Sociological Methodology. (skim)
- Volz and Heckathorn (2008) Probability Based Estimation Theory for Respondent Driven Sampling. Journal of Official Statistics.
- Gile (2011) Improved Inference for Respondent-Driven Sampling Data with Application to HIV Prevalence Estimation. Journal of the American Statistical Association.
- Goel, S. and Salganik, M.J. (2010) Assessing respondent-driven sampling. PNAS. .
- Salganik, M.J. (2012) Commentary: Respondent-driven sampling in the real world. Epidemiology.
- McCreesh et al. (2012) Evaluation of Respondent-driven Sampling Epidemiology.
- Gile, K.G., Johnston, L.G., and Salganik, M.J. (2015) Diagnostics for respondent-driven sampling. Journal of the Royal Statistical Society, Series A (Statistics in Society). .
- Baraff, McCormick, and Raftery (2016) Estimating uncertainty in respondent-driven sampling using a tree bootstrap method PNAS. .
- White, R.G. et al. (2015) Strengthening the Reporting of Observational Studies in Epidemiology for Respondent-Driven Sampling Studies: STROBE-RDS Statement Journal of Clinical Epidemiology. .
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.
- Holme and Saramäki (2012). Temporal networks, Physics Reports.
- Kossinets, G. and Watts, D.J. (2006). Empirical analysis of an evolving social network. Science.
- Lambiotte (2016). Rich get simpler PNAS.
- Sekara, Stopczynski, and Lehmann. (2016). Fundamental structures of dynamic social networks. PNAS.
- Eagle, N., Pentland, A. and Lazer, D. (2009). Inferring Social Network Structure using Mobile Phone Data. PNAS, 106(36):15274-15278 with Comment and Reply.
- Choudhury, M., Hofman, J.M., Mason, W.M., and Watts, D.J. (2010) Inferring Relevant Social Networks from Interpersonal Communication. WWW.
- Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.P. (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328:876-878.
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.