(Syllabus last updated: 2021-August-31)

Class meetings: Tuesdays, 12:30pm-3:30pm
Office hours: by appointment (please send me an email and we can find a time)
Email: feehan [at] berkeley.edu
Web: https://www.dennisfeehan.org/teaching/2021fa_demog280.html
Piazza: https://piazza.com/berkeley/fall2021/demog280sociolc273n


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, Aug 31 Course overview and background Fundamentals and background
2 Tue, Sep 7 Sampling, data collection, statistics Challenges in data collection and statistical models
3 Tue, Sep 14 Network models, connectivity, and small worlds
4 Tue, Sep 21 Social capital and SOWT
5 Tue, Sep 28 Structure and segregation
6 Tue, Oct 5 Network formation Homophily
7 Tue, Oct 12 Time
8 Tue, Oct 19 Challenges in detecting spread on a network
9 Tue, Oct 26 NO CLASS (date tentative)
10 Tue, Nov 2 Simple contagion Simple contagion
11 Tue, Nov 9 Complex contagion and social influence Complex contagion
12 Tue, Nov 16 Peer effects
13 Tue, Nov 23 Project check-in (no formal class)
14 Tue, Nov 30 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, Aug 31 - 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:

Background to read at some point in teh first couple of weeks:

  • Mark Newman, Networks: An Introduction, Second. (Oxford university press, 2018), ch. 6 and 7. - some mathematical background

We won’t explicitly discuss the Newman book chapters in class, but they also worth reading at some point; they describe 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 7

Readings to discuss:

  • Related to McPherson et al (2006) [from last week]
  • N. Eagle, A. S. Pentland, and D. Lazer, “Inferring Friendship Network Structure by Using Mobile Phone Data,” Proceedings of the National Academy of Sciences 106, no. 36 (2009): 15274—15278, http://www.pnas.org/content/106/36/15274.short.
  • 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.
  • 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.
  • [READ ABSTRACT] Cathleen McGrath, Jim Blythe, and David Krackhardt, “The Effect of Spatial Arrangement on Judgments and Errors in Interpreting Graphs,” Social Networks 19, no. 3 (1997): 223–242, http://www.sciencedirect.com/science/article/pii/S0378873396002997.
  • check out hive plots

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):

Network models, connectivity, and small worlds

Tue, Sep 14 - Network models, connectivity, and small worlds

Readings to discuss:

Some fairly recent online discussion of the small world hypothesis:

Background and related:

Social capital and SOWT

Tue, Sep 21

Readings we will discuss:

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


Communities and signed networks

Tue, Sep 28

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

Network formation, homophily

Tue, Oct 5 - Homophily - network formation based on similarity

  • 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.
  • 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.

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

Tue, Oct 12 - 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):

Challenges in understanding spread on a network

Tue, Oct 19

  • N. A. Christakis and J. H. Fowler, “The Spread of Obesity in a Large Social Network over 32 Years,” New England Journal of Medicine 357, no. 4 (2007): 370—379, http://www.nejm.org/doi/full/10.1056/nejmsa066082.
  • Cosma Rohilla Shalizi and Andrew C. Thomas, “Homophily and Contagion Are Generically Confounded in Observational Social Network Studies,” Sociological Methods & Research 40, no. 2 (2011): 211–239, http://smr.sagepub.com/content/40/2/211.short.
  • David A Kim et al., “Social Network Targeting to Maximise Population Behaviour Change: A Cluster Randomised Controlled Trial,” The Lancet 386, no. 9989 (July 2015): 145–153, doi:10.1016/S0140-6736(15)60095-2.
  • (At least one more reading, TBA)

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


Simple contagion

Tue, Nov 2

  • Nicholas A. Christakis and James H. Fowler, “Social Network Sensors for Early Detection of Contagious Outbreaks,” PloS One 5, no. 9 (2010): e12948, http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012948.
  • Stéphane Helleringer and Hans-Peter Kohler, “Sexual Network Structure and the Spread of HIV in Africa: Evidence from Likoma Island, Malawi:” AIDS 21, no. 17 (November 2007): 2323–2332, doi:10.1097/QAD.0b013e328285df98.
  • Per Block et al., “Social Network-Based Distancing Strategies to Flatten the COVID-19 Curve in a Post-Lockdown World,” Nature Human Behaviour 4, no. 6 (June 2020): 588–596, doi:10.1038/s41562-020-0898-6.
  • Dennis M. Feehan and Ayesha S. Mahmud, “Quantifying Population Contact Patterns in the United States During the COVID-19 Pandemic,” Nature Communications 12, no. 1 (2021): 1–9.
  • (Perhaps one more reading, TBA)

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

Complex contagion and social influence

Tue, Nov 9 - Complex contagion

  • Mark Granovetter, “Threshold Models of Collective Behavior,” American Journal of Sociology 83, no. 6 (1978): 1420–1443, doi:10.2307/2778111.
  • Paul DiMaggio and Filiz Garip, “How Network Externalities Can Exacerbate Intergroup Inequality,” American Journal of Sociology 116, no. 6 (May 2011): 1887–1933, doi:10.1086/659653.
  • Damon Centola, “The Social Origins of Networks and Diffusion,” American Journal of Sociology 120, no. 5 (2015): 1295–1338, http://www.jstor.org/stable/10.1086/681275.
  • Johan Ugander et al., “Structural Diversity in Social Contagion,” Proceedings of the National Academy of Sciences 109, no. 16 (2012): 5962–5966, http://www.pnas.org/content/109/16/5962.short.

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).
  • Michael W. Macy and Anna Evtushenko, “Threshold Models of Collective Behavior II: The Predictability Paradox and Spontaneous Instigation,” Sociological Science 7 (December 2020): 628–648, doi:10.15195/v7.a26.

Especially relevant for demography:

Tue, Nov 16 - 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

Political networks

  • 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 (2015/ed): 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.
  • Diana C. Mutz, “The Consequences of Cross-Cutting Networks for Political Participation,” American Journal of Political Science (2002): 838–855.
  • Jennifer M. Larson and Janet I. Lewis, “Ethnic Networks,” American Journal of Political Science 61, no. 2 (2017): 350–364, doi:10.1111/ajps.12282.
  • 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.

Collaboration and cooperation

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.