(Syllabus last updated: 2022-November-03)

Class meetings: Thursdays, 10:00am-1:00pm
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/2022fa_demog280.html
Ed: https://edstem.org/us/courses/25666/discussion/


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 Thu, Aug 25 Course overview and background Fundamentals and background
2 Thu, Sep 1 Sampling, data collection, statistics Challenges in data collection and statistical models
3 Thu, Sep 8 Network models, connectivity, and small worlds
4 Thu, Sep 15 Social capital and SOWT Classics
5 Thu, Sep 22 Contemporary
6 Thu, Sep 29 Structure and segregation
7 Thu, Oct 6 Network formation Homophily
8 Thu, Oct 13 Time
9 Thu, Oct 20 Simple contagion Simple contagion
10 Thu, Oct 27 Complex contagion and social influence Complex contagion
11 Thu, Nov 3 Peer effects
12 Thu, Nov 10 Project check-in
13 Thu, Nov 17 Challenges in detecting spread on a network
14 Thu, Nov 24 THANKSGIVING (no class)
15 Thu, 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

Thu, Aug 25 - 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.

Background readings:

Readings to discuss:

  • Scott L. Feld, “Why Your Friends Have More Friends Than You Do,” American Journal of Sociology 96, no. 6 (May 1991): 1464–1477, http://www.jstor.org/stable/2781907.
  • Miller McPherson, Lynn Smith-Lovin, and Matthew E. Brashears, “Social Isolation in America: Changes in Core Discussion Networks over Two Decades,” American Sociological Review 71, no. 3 (2006): 353–375, http://asr.sagepub.com/content/71/3/353.short.
  • Nir Grinberg et al., “Fake News on Twitter During the 2016 U.S. Presidential Election,” Science 363, no. 6425 (January 2019): 374–378, doi:10.1126/science.aau2706.

More background to read at some point in the 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

Thu, Sep 1

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

Thu, Sep 8 - 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

Thu, Sep 15 - Classics

Readings we will discuss:

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


  • Douglas S. Massey, “Social Structure, Household Strategies, and the Cumulative Causation of Migration,” Population Index (1990): 3–26, http://www.jstor.org/stable/3644186.

Thu, Sep 22 - Contemporary

Readings we will discuss:

  • Sinan Aral and Marshall Van Alstyne, “The Diversity-Bandwidth Trade-off,” American Journal of Sociology 117, no. 1 (July 2011): 90–171, doi:10.1086/661238.
  • Raj Chetty et al., “Social Capital I: Measurement and Associations with Economic Mobility,” Nature 608, no. 7921 (August 2022): 108–121, doi:10.1038/s41586-022-04996-4.
  • Raj Chetty et al., “Social Capital II: Determinants of Economic Connectedness,” Nature 608, no. 7921 (August 2022): 122–134, doi:10.1038/s41586-022-04997-3.

Also, check out the social capital atlas.

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

  • J. P. Onnela et al., “Structure and Tie Strengths in Mobile Communication Networks,” Proceedings of the National Academy of Science, USA 104, no. 18 (2007): 7332–7336, https://www.pnas.org/content/104/18/7332.short.
  • Eagle, Macy, and Claxton, “Network Diversity and Economic Development.”
  • Comments on Aral and Van Alstyne, “The Diversity-Bandwidth Trade-off.”
    • Jeroen Bruggeman, “The Strength of Varying Tie Strength: Comment on Aral and Van Alstyne,” American Journal of Sociology 121, no. 6 (May 2016): 1919–1930, doi:10.1086/686267.
    • Sinan Aral, “The Future of Weak Ties,” American Journal of Sociology 121, no. 6 (May 2016): 1931–1939, doi:10.1086/686293.
  • Michael Bailey et al., “The Economic Effects of Social Networks: Evidence from the Housing Market,” Journal of Political Economy 126, no. 6 (2018): 2224–2276.
  • Patrick S. Park, Joshua E. Blumenstock, and Michael W. Macy, “The Strength of Long-Range Ties in Population-Scale Social Networks,” Science 362, no. 6421 (December 2018): 1410–1413, doi:10.1126/science.aau9735.
  • Kauppi et al., “Characteristics of Social Networks and Mortality Risk.”
  • Eng et al., “Social Ties and Change in Social Ties in Relation to Subsequent Total and Cause-specific Mortality and Coronary Heart Disease Incidence in Men.”
  • Karthik Rajkumar et al., “A Causal Test of the Strength of Weak Ties,” Science 377, no. 6612 (September 2022): 1304–1310, doi:10.1126/science.abl4476.


Structure and segregation

Thu, Sep 29

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

Network formation

Thu, Oct 6 - 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:

Thu, Oct 13 - 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):

Simple contagion

(Readings for this week not yet finalized)

Thu, Oct 20

  • 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.
  • 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.

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

Complex contagion and social influence

Thu, Oct 27 - 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.
  • Jonas L. Juul and Johan Ugander, “Comparing Information Diffusion Mechanisms by Matching on Cascade Size,” Proceedings of the National Academy of Sciences 118, no. 46 (November 2021): e2100786118, doi:10.1073/pnas.2100786118.

Especially relevant for demography:

Thu, Nov 3 - 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.
  • D. Centola, “The Spread of Behavior in an Online Social Network Experiment,” Science 329, no. 5996 (2010): 1194—1197, http://www.sciencemag.org/content/329/5996/1194.short.
  • 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.

Project pitches

Thu, Nov 10

We will meet in class and each of us will spend a few minutes explaining what we plan to work on for the final project. There will be an opportunity for some peer feedback and discussion (as much as time allows).

Challenges in understanding spread on a network

Thu, Nov 17

  • 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.

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


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.