(Syllabus last updated: 2020-November-10)

Class meetings: Tuesdays, 2-5pm
Office hours: by appointment (please send me an email and we can find a time)
Email: feehan [at] berkeley.edu
Web: www.dennisfeehan.org/teaching/2020fa_demog280.html
Piazza: https://piazza.com/class/kecbra1oig1405


This course provides a broad introduction to the empirical and theoretical study of social networks. We will cover classic and contemporary studies, beginning with fundamental definitions and models, and then moving through a range of topics, including models of network formation and structure (homophily, foci, communities); dynamic processes on networks (contagion, influence, and disease models); collaborative networks; personal networks; online networks; and network sampling and data collection. The course material is intended to be of interest to students from a wide range of disciplinary backgrounds, including demography, sociology, statistics, computer science, and related fields.

Please re-check the syllabus before you start each week’s reading; it will be updated as the semester progresses

Week Date Theme Topic Resources
1 Tue, Sep 1 Course overview and background Fundamentals and background
2 Tue, Sep 8 Sampling, data collection, statistics Data collection challenges
3 Tue, Sep 15 Statistics and networks
4 Tue, Sep 22 Network models, connectivity, and small worlds
5 Tue, Sep 29 Communities, social capital, SOWT Strength of weak ties and foci
6 Tue, Oct 6 Structure and segregation
7 Tue, Oct 13 Network formation Homophily
8 Tue, Oct 20 Time
9 Tue, Oct 27 Political networks
10 Tue, Nov 3 Contagion and influence Simple contagion
11 Tue, Nov 10 Complex contagion
12 Tue, Nov 17 Peer effects
13 Tue, Nov 24 Project check-in (no formal class)
14 Tue, Dec 1 Mini-conference

Requirements and assignments

The requirements of the class are designed to achieve two goals: the first goal is to become familiar with some classic and contemporary research about social networks through reading papers and discussing them; and the second goal is to write a research paper. You should think of the research paper as the first draft of a project that you might be able to continue working on beyond this class.

NB: Please read each week’s articles in the order they are listed on the syllabus

Detailed schedule

Fundamentals and background

Tue, Sep 1 - Fundamentals and background

This is an unusual week, since it’s our first class meeting. The first three readings are overviews of social networks from different perspectives; then, there are three studies that exemplify the diversity of social networks research.

Readings to discuss:

We won’t explicitly discuss chapter 7 of the Newman book in class, but it’s also worth reading at some point; it describes several different network measures that are often mentioned in the literature.

OPTIONAL: The wrap-up papers at the end of the syllabus give a good overview of the study of social networks. We won’t explicitly discuss them in class, but they would be helpful to read at some point during the semester.

Related, but we won’t have time to discuss in class:

Sampling, data collection, statistics

Tue, Sep 8

Readings to discuss:

I’ll talk a little bit about random graph models; if you want extra background, the Newman chapter is a good reference:

  • Newman, Networks, ch. 11. - Poisson random graph models (NB: this is ch. 12 in the first edition)

Background and related (we won’t discuss):

  • P. V. Marsden, “Network Data and Measurement,” Annual Review of Sociology (1990): 435—463, http://www.jstor.org/stable/10.2307/2083277.
  • Peter V. Marsden, “Recent Developments in Network Measurement,” in Models and Methods in Social Network Analysis, ed. Peter J. Carrington, John Scott, and Stanley Wasserman (Cambridge University Press, 2005), 8–30.
  • Matthew E. Brashears, “’Trivial’ Topics and Rich Ties: The Relationship Between Discussion Topic, Alter Role, and Resource Availability Using the ‘Important Matters’ Name Generator,” Sociological Science 1 (November 2014): 493–511, doi:10.15195/v1.a27.
  • Peter Bearman and Paolo Parigi, “Cloning Headless Frogs and Other Important Matters: Conversation Topics and Network Structure,” Social Forces 83, no. 2 (December 2004): 535–557, doi:10.1353/sof.2005.0001.
  • Byungkyu Lee and Peter Bearman, “Important Matters in Political Context,” Sociological Science 4 (2017): 1–30, https://www.sociologicalscience.com/articles-v4-1-1/.
  • Sarah K. Cowan and Delia Baldassarri, “‘It Could Turn Ugly’: Selective Disclosure of Attitudes in Political Discussion Networks,” Social Networks (2017), https://www.sciencedirect.com/science/article/pii/S037887331630404X.
  • Mario Luis Small, Someone to Talk To (Oxford University Press, 2017).

Tue, Sep 15 - Sampling, data collection, statistics

Readings to discuss:

  • (we will briefly talk about visualization and hive plots, which we didn’t get to last week)
  • Peter D. Hoff, Adrian E. Raftery, and Mark S. Handcock, “Latent Space Approaches to Social Network Analysis,” Journal of the American Statistical Association 97, no. 460 (2002): 1090–1098, http://www.tandfonline.com/doi/abs/10.1198/016214502388618906.
  • Sharad Goel and Matthew J. Salganik, “Assessing Respondent-Driven Sampling,” Proceedings of the National Academy of Sciences 107, no. 15 (2010): 6743–6747, http://www.pnas.org/content/107/15/6743.short.
  • Dennis M. Feehan, Mary Mahy, and Matthew J. Salganik, “The Network Survival Method for Estimating Adult Mortality: Evidence from a Survey Experiment in Rwanda,” Demography 54, no. 4 (2017): 1503–1528, https://link.springer.com/article/10.1007/s13524-017-0594-y. (appendix is optional)
  • Tian Zheng, Matthew J. Salganik, and Andrew Gelman, “How Many People Do You Know in Prison?: Using Overdispersion in Count Data to Estimate Social Structure in Networks,” Journal of the American Statistical Association 101, no. 474 (June 2006): 409–423, doi:10.2307/27590705.
  • Joël Mossong et al., “Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases,” PLoS Medicine 5, no. 3 (2008): e74, http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0050074.

Background and related (we won’t discuss):

Network models, connectivity, and small worlds

Tue, Sep 22 - Network models, connectivity, and small worlds

Readings to discuss:

Some fairly recent online discussion of the small world hypothesis:

Background and related:

Communities, social capital, SOWT

Tue, Sep 29

Readings we will discuss:

Also interesting (but we won’t have time to discuss in class):


Tue, Oct 6

Also interesting (but we won’t have time to discuss in class):

Network formation, homophily

Tue, Oct 13 - Homophily

  • Gueorgi Kossinets and Duncan J. Watts, “Empirical Analysis of an Evolving Social Network,” Science 311, no. 5757 (January 2006): 88–90, doi:10.1126/science.1116869.
  • G. Kossinets and D. J. Watts, “Origins of Homophily in an Evolving Social Network,” American Journal of Sociology 115, no. 2 (2009): 405—450, http://www.jstor.org/stable/10.1086/599247?ai=s6\&af=R.
  • Sergio Currarini, Matthew O. Jackson, and Paolo Pin, “Identifying the Roles of Race-Based Choice and Chance in High School Friendship Network Formation,” Proceedings of the National Academy of Sciences 107, no. 11 (2010): 4857–4861, http://www.pnas.org/content/107/11/4857.short.
  • Elizabeth E. Bruch and M. E. J. Newman, “Aspirational Pursuit of Mates in Online Dating Markets,” Science Advances 4, no. 8 (August 2018): eaap9815, doi:10.1126/sciadv.aap9815.

Also interesting, but we will not have time to discuss:

Tue, Oct 20 - Network formation over time

Some recent online discussions of the power law debate (not required reading):

Also interesting (but we won’t have time to discuss in class):

Political networks

Tue, Oct 27

  • Diana C. Mutz, “Cross-Cutting Social Networks: Testing Democratic Theory in Practice,” American Political Science Review 96, no. 1 (2002): 111–126, http://journals.cambridge.org/production/action/cjoGetFulltext?fulltextid=208465.
  • Pablo Barberá, “Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data,” Political Analysis 23, no. 1 (n.d.): 76–91, doi:10.1093/pan/mpu011.
  • Sandra González-Bailón and Ning Wang, “Networked Discontent: The Anatomy of Protest Campaigns in Social Media,” Social Networks 44 (January 2016): 95–104, doi:10.1016/j.socnet.2015.07.003.
  • Andrew Guess, Jonathan Nagler, and Joshua Tucker, “Less Than You Think: Prevalence and Predictors of Fake News Dissemination on Facebook,” Science Advances 5, no. 1 (January 2019): eaau4586, doi:10.1126/sciadv.aau4586.

Also interesting (but we won’t have time to discuss):

  • Diana C. Mutz, “The Consequences of Cross-Cutting Networks for Political Participation,” American Journal of Political Science (2002): 838–855.
  • Paul Allen Beck et al., “The Social Calculus of Voting: Interpersonal, Media, and Organizational Influences on Presidential Choices,” The American Political Science Review 96, no. 1 (2002): 57–73, https://www.jstor.org/stable/3117810.
  • Matthew Gentzkow and Jesse M. Shapiro, “Ideological Segregation Online and Offline,” The Quarterly Journal of Economics 126, no. 4 (November 2011): 1799–1839, doi:10.1093/qje/qjr044.
  • James H. Fowler, “Legislative Cosponsorship Networks in the US House and Senate,” Social Networks 28, no. 4 (October 2006): 454–465, doi:10.1016/j.socnet.2005.11.003.
  • Marco Battaglini, Valerio Leone Sciabolazza, and Eleonora Patacchini, “Effectiveness of Connected Legislators,” American Journal of Political Science n/a, no. n/a (2020), doi:10.1111/ajps.12518.
  • Elisabeth Noelle-Neumann, “Turbulences in the Climate of Opinion: Methodological Applications of the Spiral of Silence Theory,” Public Opinion Quarterly 41, no. 2 (January 1977): 143–158, doi:10.1086/268371.
  • Dietram A. Scheufle and Patricia Moy, “Twenty-Five Years of the Spiral of Silence: A Conceptual Review and Empirical Outlook,” International Journal of Public Opinion Research 12, no. 1 (March 2000): 3–28, doi:10.1093/ijpor/12.1.3.
  • Pablo Barberá et al., “Who Leads? Who Follows? Measuring Issue Attention and Agenda Setting by Legislators and the Mass Public Using Social Media Data,” American Political Science Review 113, no. 4 (November 2019): 883–901, doi:10.1017/S0003055419000352.
  • Michela Del Vicario et al., “The Spreading of Misinformation Online,” Proceedings of the National Academy of Sciences 113, no. 3 (January 2016): 554–559, doi:10.1073/pnas.1517441113.
  • Delia Baldassarri and Peter Bearman, “Dynamics of Political Polarization,” American Sociological Review 72, no. 5 (October 2007): 784–811, doi:10.1177/000312240707200507.

Contagion and influence

Tue, Nov 3 - Simple contagion and epidemics; methodological challenges

Also interesting, but we will not have time to discuss:

Tue, Nov 10 - Complex contagion

Also interesting, but we will not have time to discuss

  • Duncan J Watts, “A Simple Model of Global Cascades on Random Networks,” Proceedings of the National Academy of Sciences of the United States of America 99, no. 9 (April 2002): 5766–5771, doi:10.1073/pnas.082090499.
  • D. J. Watts and P. S. Dodds, “Influentials, Networks, and Public Opinion Formation,” Journal of Consumer Research 34, no. 4 (2007): 441—458, http://www.jstor.org/stable/10.1086/518527.
  • Damon Centola and Michael Macy, “Complex Contagions and the Weakness of Long Ties,” American Journal of Sociology 113, no. 3 (November 2007): 702–734, http://www.jstor.org/stable/10.1086/521848.
  • Damon Centola, How Behavior Spreads: The Science of Complex Contagions (Princeton University Press, 2018).

Especially relevant for demography:

Tue, Nov 17 - Peer effects

Also interesting, but we won’t have time to discuss:

  • Elizabeth Levy Paluck, Hana Shepherd, and Peter M. Aronow, “Changing Climates of Conflict: A Social Network Experiment in 56 Schools,” Proceedings of the National Academy of Sciences 113, no. 3 (2016): 566–571, http://www.pnas.org/content/113/3/566.short.
  • Bruce Sacerdote, Peer Effects with Random Assignment: Results for Dartmouth Roommates (National bureau of economic research, 2000), http://www.nber.org/papers/w7469.
  • Hans-Peter Kohler, Jere R. Behrman, and Susan C. Watkins, “Social Networks and HIV/AIDS Risk Perceptions,” Demography 44, no. 1 (2007): 1–33, http://link.springer.com/article/10.1353/dem.2007.0006.
  • Eytan Bakshy et al., “The Role of Social Networks in Information Diffusion,” in Proceedings of the 21st International Conference on World Wide Web, 2012, 519–528, http://dl.acm.org/citation.cfm?id=2187907.
  • Eytan Bakshy, Dean Eckles, and Michael S. Bernstein, “Designing and Deploying Online Field Experiments,” in Proceedings of the 23rd International Conference on World Wide Web (ACM, 2014), 283–292, http://dl.acm.org/citation.cfm?id=2567967.
  • Dean Eckles, Brian Karrer, and Johan Ugander, “Design and Analysis of Experiments in Networks: Reducing Bias from Interference,” arXiv Preprint arXiv:1404.7530 (2014), http://arxiv.org/abs/1404.7530.
  • Eytan Bakshy et al., “Social Influence in Social Advertising: Evidence from Field Experiments,” in Proceedings of the 13th ACM Conference on Electronic Commerce (ACM, 2012), 146–161, http://dl.acm.org/citation.cfm?id=2229027.


For the mini-conference, we will each give a brief presentation of our paper. There’s no specific reading for this week.


Optional wrap-up:

Additional topics

Collaboration and cooperation

Also interesting, but we will not have time to discuss:

Religious Accommodations

Requests to accommodate a student’s religious creed by scheduling tests or examinations at alternative times should be submitted directly to the instructor. Reasonable common sense, judgment and the pursuit of mutual goodwill should result in the positive resolution of scheduling conflicts. The regular campus appeals process applies if a mutually satisfactory arrangement cannot be achieved.

Statement on Academic Freedom

Both students and instructors have rights to academic freedom. Please respect the rights of others to express their points of view in the classroom.

DSP Accommodations

Please see the instructor to discuss accommodations for physical disabilities, medical disabilities and learning disabilities.

Student Resources

The Student Learning Center provides a wide range of resources to promote learning and academic success for students. For information regarding these services, please consult the Student Learning Center Website: https://slc.berkeley.edu/

Academic Integrity

The high academic standard at the University of California, Berkeley, is reflected in each degree that is awarded. As a result, every student is expected to maintain this high standard by ensuring that all academic work reflects unique ideas or properly attributes the ideas to the original sources.

These are some basic expectations of students with regards to academic integrity:

  • Any work submitted should be your own individual thoughts, and should not have been submitted for credit in another course unless you have prior written permission to re-use it in this course from this instructor.
  • All assignments must use “proper attribution,” meaning that you have identified the original source and extent or words or ideas that you reproduce or use in your assignment. This includes drafts and homework assignments!
  • If you are unclear about expectations, ask your instructor or GSI.
  • Do not collaborate or work with other students on assignments or projects unless you have been given permission or instruction to do so.