Readings are posted on Canvas.

In this 6-week seminar we hope to cover some of the history and major themes in the study of social networks. This is meant to be an on-ramp not a comprehensive survey.

Week 1: The small world problem

• Watts (2003). Six Degrees: The Science of a Connected Age, Introduction & Chapters 1-3.
• Milgram (1967). The small world problem. Psychology Today, 1:62-67.
• Travers and Milgram (1969). An experimental study of the small world problem. Sociometry, 32(4):425-443.
• Granovetter (2003). Ignorance, knowledge, and outcomes in a small world. Science, 301:773-774.
• Dodds, Muhamad, and Watts (2003). An experimental study of search in a global social networks. Science, 301:827-829.
• Watts and Strogatz (1998). Collective dynamics of ‘small-world’ networks. Nature, 393:440-442.
• Victor (2011). Scientific Communication As Sequential Art.
• Watts, Dodds, and Newman (2002). Identity and Search in Social Networks. Science, 296:1302-1305.

Week 2: What do social networks look like?

• Newman (2018). Chapter 4: Social networks. Networks: An Introduction.
• Freeman (2000). Visualizing Social Networks. Journal of Social Structure, 1(1).
• Newman (2018). Chapter 7: Measures and metrics. Networks: An Introduction.
• Bearman et al. (2004). Chains of affection: The structure of adolescent romantic and sexual networks. American Journal of Sociology, 110(1):44-91.
• Angrist and Sacerdote (2022). The social connections that shape economic prospects. Nature, 608:37-38.
• Chetty et al. (2022). Social capital I: measurement and associations with economic mobility. Nature, 608:108-121.
• Milo et al. (2002). Network Motifs: Simple Building Blocks of Complex Networks. Science, 298:824-827.
• Feld (1991). Why Your Friends Have More Friends Than You Do. American Journal of Sociology, 96(6):1464-1477.

Week 3: Where do social networks come from? Models and mechanisms

• Newman (2018). Chapter 11: Random graphs. Networks: An Introduction. [This is not a course on graph theory so the mathematical derivations are not required for this class, but understanding them might be required for your research.]
• Newman (2018). Chapter 12: Configuration model. Networks: An Introduction. [This is not a course on graph theory so the mathematical derivations are not required for this class, but understanding them might be required for your research.]
• Barabasi and Albert (1999). Emergence of scaling in random networks. Science, 286:509-512.
• Chetty et al. (2022). Social capital II: determinants of economic connectedness. Nature, 608:122-134.
• Feld (1981). The focused organization of social ties. American Journal of Sociology, 86(5):1015-1035.
• Kossinets and Watts (2006). Empirical analysis of an evolving social network. Science, 311(5757):88-90.

Week 4: How do things spread on social networks?

• Granovetter (1973). The strength of weak ties. American Journal of Sociology, 78(6):1360-1380.
• Granovetter (1978). Threshold Models of Collective Behavior. American Journal of Sociology, 83(6):1420-1443.
• Watts (2003). Six Degrees: The Science of a Connected Age, Chapter 8.
• Watts (2002). A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences, 99(9):5766-5771.
• Centola and Macy (2007). Complex contagions and the weakness of long ties. American Journal of Sociology, 113(3):702-734.
• Centola (2010). The spread of behavior in an online social network experiment. Science, 329:1194-1197.
• Wan, Riedl, and Lazer (2025). Diffusion of complex contagions is shaped by a trade-off between reach and reinforcement. Proceedings of the National Academy of Sciences, 122(28):e2422892122.

Week 5: What is happening with online social networks?

• Stroud et al. (2020). A Proposal for Understanding Social Media’s Impact on Elections: Rigorous, Peer-Reviewed Scientific Research Blog post.
• Thorp (2023). Face value Science, (381)6656:359.
• Guess et al. (2023). How do social media feed algorithms affect attitudes and behavior in an election campaign? Science, 381(6656):398-404.
  • Kupferschmidt (2024). A study found Facebook’s algorithm didn’t promote political polarization. Critics have doubts. Science.
  • Bagchi et al. (2024). Social media algorithms can curb misinformation, but do they? Science, eletter.
  • Guess et al. (2024). Response to “Social media algorithms can curb misinformation, but do they?” Science, eletter.
  • (2023) US 2020 Facebook & Instagram Election Study: Frequently Asked Questions (FAQ).
  • Dimmery (2025) What was US2020? The dialectics of big internet research, Blog post.
• Wagner (2023) Independence by permission: Industry–academy collaboration explores the 2020 US election Science (381)6656:388-391.
• Guess et al. (2023). Reshares on social media amplify political news but do not detectably affect beliefs or opinions. Science, 381(6656):404-408.
• González-Bailón et al. (2023). Asymmetric ideological segregation in exposure to political news on Facebook. Science, 381(6656):392-398.
• Allcott et al. (2024). The effects of Facebook and Instagram on the 2020 election: A deactivation experiment. Proceedings of the National Academy of Sciences, 121(21):e2321584121.
• Researchers can apply to access deidentified, individual-level data from any of the published papers at the Social Media Archive

Special guest from the Research Accelerator

Week 6: Networks, sampling, and hidden populations

• Salganik and Heckathorn (2004). Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling. Sociological Methodology, 34:193-240.
• Abdul-Quader et al. (2006). Effectiveness of Respondent-Driven Sampling for Recruiting Drug Users in New York City: Findings from a Pilot Study. Journal of Urban Health, 83:459-476.
• Goel and Salganik (2010). Assessing Respondent-Driven Sampling. Proceedings of the National Academy of Sciences, 107:6743-6747.
• Gile et al. (2015). Diagnostics for Respondent-Driven Sampling. Journal of the Royal Statistical Society, Series A, 178:241-269.
• Bernard et al. (2010). Counting Hard-to-Count Populations: The Network Scale-up Method for Public Health. Sexually Transmitted Infections, 86(Suppl 2):ii11-ii15.
• Feehan and Salganik (2016). Generalizing the Network Scale-Up Method. Sociological Methodology, 46:153-186.
• Feehan et al. (2016). Quantity Versus Quality: A Survey Experiment to Improve the Network Scale-up Method. American Journal of Epidemiology, 183:747-757.
• Feehan and Salganik (2025). Validating survey-based estimates of adult mortality with high-quality vital statistics: Evidence from 27 cities Working paper.