(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
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
Please re-check the syllabus before you start each week’s
reading; it will be updated as the semester progresses
Thu, Aug 25
Course overview and background
Fundamentals and background
Thu, Sep 1
Sampling, data collection, statistics
Challenges in data collection and statistical models
Thu, Sep 8
Network models, connectivity, and small
Thu, Sep 15
Social capital and SOWT
Thu, Sep 22
Thu, Sep 29
Structure and segregation
Thu, Oct 6
Thu, Oct 13
Thu, Oct 20
Thu, Oct 27
Complex contagion and social
Thu, Nov 3
Thu, Nov 10
Thu, Nov 17
Challenges in detecting spread on a network
Thu, Nov 24
THANKSGIVING (no class)
Thu, Dec 1
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.
Reading and class participation
Each week, you should read the assigned materials and show up to
class prepared to discuss them. One of the weeks you will also
be in charge of organizing the discussion (possibly with a
partner). To lead the discussion, I suggest that you make a
short slide deck summarizing each of the readings (similar to the papers
to present, below), and you think of a few questions to prompt
discussions for each reading.
For 6 of the class meetings, please write a short response memo (1 to
2 pages) and post it on the Ed thread for that week. (There is an Ed tag
for each week of the semester). You can pick which of the 6 weeks you
write the memos. These memos should not take an enormous amount of time
to write. The main goal of these memos is to help you focus your
thoughts about the reading prior to our group discussion; to help us get
an understanding of what parts of the reading our discussion should
focus on; and to serve as a reference for you in the future. The format
of the memos is open, but at a minimum I would like you to be sure to
(1) quickly summarize how the readings relate to one another (if you
think they do); and (2) briefly describe at least one research idea that
the readings generated for you (this could be a single sentence, or the
entire memo; it’s up to you). The response memos are due by
noon on the day before each class.
Papers to present
Each week, there will be a list of readings that we will not have
time to discuss as a group. For 3 of the class meetings, please choose
one of these papers, read it, and briefly present it to the class. You
should ‘claim’ the paper you want to present by posting to a Ed thread
for the given week, to ensure that two people don’t end up preparing a
presentation for the same paper. (Also, if you have a paper that is
on-topic, but not on the syllabus, you can ask me to present that one
instead - I will typically say yes, as long as it is relevant to the
discussion that week.) These presentations should be around 5 minutes
each. Please plan to make slides or find some other way to help the
class understand the paper’s main findings. The goals of these
presentations are (1) to give you some practice taking a deep dive into
a networks research paper; (2) to give you some practice distilling
technical results for a broader audience; (3) to give you and the class
some exposure to cutting edge ideas in social networks; (4) to give you
an opportunity to spend time on papers that are particularly useful for
You will write a short (10-20 pages) research paper to conclude the
class. Leading up to the end of the class, you will submit a brief
proposal (1 paragraph to 1 page) that describes the paper you plan to
work on. The purpose of the proposal is to give you some feedback on the
initial idea / data source / etc before you invest a lot of time writing
an actual paper. Your final paper should identify an important problem
to be studied, briefly review the related literature, describe your
proposed research design, and present some (possibly preliminary)
The purpose of this paper is to connect the topics of this class to your
actual research, so my hope is that this will be an opportunity to get
some feedback on an idea you care about, and that you might continue to
pursue beyond class. We will have a mini-conference with short
presentations for each project at the end of the semester.
The final paper is due at 5pm on Friday, December
NB: Please read each week’s articles in the order they
are listed on the syllabus
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.
- Stephen P. Borgatti et al., “Network
Analysis in the Social Sciences,” Science 323, no. 5916
(2009): 892–895, http://science.sciencemag.org/content/323/5916/892.short.
- C. T Butts, “Revisiting the Foundations
of Network Analysis,” Science 325, no. 5939 (2009):
- David Lazer, “Networks in
Political Science: Back to the
Future,” PS: Political Science and Politics
44, no. 1 (2011): 61–68, https://www.jstor.org/stable/40984485.
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):
- 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
- 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:
- Christine A. Bachrach, “Culture and
Demography: From Reluctant Bedfellows to Committed
Partners,” Demography 51, no. 1 (2014): 3–25, http://link.springer.com/article/10.1007/s13524-013-0257-6.
- Hans-Peter Kohler et al., “The
Social and the Sexual: Networks
in Contemporary Demographic Research” (2013), http://repository.upenn.edu/psc_working_papers/41/.
- Mustafa Emirbayer, “Manifesto for a
Relational Sociology,” American Journal of Sociology
103, no. 2 (1997): 281–317, http://www.jstor.org/stable/10.1086/231209.
- David Lazer et al., “Computational
Social Science,” Science 323, no. 5915
(February 2009): 721–723, doi:10.1126/science.1167742.
- Robin IM Dunbar and Susanne Shultz,
“Evolution in the Social Brain,” Science 317, no.
5843 (2007): 1344–1347, http://www.sciencemag.org/content/317/5843/1344.short.
- Alistair Sutcliffe et al.,
“Relationships and the Social Brain: Integrating
Psychological and Evolutionary Perspectives,” British Journal
of Psychology 103, no. 2 (2012): 149–168, http://onlinelibrary.wiley.com/doi/10.1111/j.2044-8295.2011.02061.x/full.
- Nathan Eagle and Alex Sandy Pentland,
“Eigenbehaviors: Identifying Structure in
Routine,” Behavioral Ecology and Sociobiology 63, no. 7
(2009): 1057–1066, http://link.springer.com/article/10.1007/s00265-009-0739-0.
- Matthew O. Jackson, “The
Friendship Paradox and Systematic Biases in
Perceptions and Social Norms,”
Journal of Political Economy 127, no. 2 (October 2018):
Sampling, data collection, statistics
Thu, Sep 1
Readings to discuss:
- Related to McPherson et al (2006) [from last week]
- Claude S. Fischer, “The 2004 GSS
Finding of Shrunken Social Networks: An
Artifact?” American Sociological Review 74, no. 4
(August 2009): 657–669, doi:10.1177/000312240907400408.
- [SKIM] M. McPherson, L. Smith-Lovin, and M. E
Brashears, “Models and Marginals: Using Survey Evidence to Study
Social Networks,” American Sociological Review 74, no. 4
(2009): 670—681, http://asr.sagepub.com/content/74/4/670.short.
- [READ ABSTRACT] Anthony Paik and Kenneth
Sanchagrin, “Social Isolation in America An
Artifact,” American Sociological Review (2013):
- 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
Background and related (we won’t discuss):
- S. Goel, W. Mason, and D. J Watts,
“Real and Perceived Attitude Agreement in Social Networks.”
Journal of Personality and Social Psychology 99, no. 4 (2010):
- P. V. Marsden, “Network Data and
Measurement,” Annual Review of Sociology (1990):
- Matthew E. Brashears, “Small Networks
and High Isolation? A Reexamination of
American Discussion Networks,” Social
Networks 33, no. 4 (October 2011): 331–341, doi:10.1016/j.socnet.2011.10.003.
- 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):
- 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).
- 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):
(appendix is optional)
- Tyler H. McCormick, Matthew J. Salganik, and
Tian Zheng, “How Many People Do You Know?:
Efficiently Estimating Personal Network Size,”
Journal of the American Statistical Association 105, no. 489
(2010): 59–70, doi:10.1198/jasa.2009.ap08518.
- Tyler H. McCormick and Tian Zheng,
“Latent Demographic Profile Estimation in Hard-to-Reach
Groups,” The Annals of Applied Statistics 6, no. 4
(December 2012): 1795–1813, doi:10.1214/12-AOAS569.
- M. S. Handcock and K. J. Gile,
“Modeling Social Networks from Sampled Data,” The
Annals of Applied Statistics 4, no. 1 (2010): 5—25, http://projecteuclid.org/euclid.aoas/1273584445.
- P. D. Hoff, “Bilinear Mixed-Effects
Models for Dyadic Data,” Journal of the American Statistical
Association 100, no. 469 (2005): 286–295, https://amstat.tandfonline.com/doi/abs/10.1198/016214504000001015?casa_token=yF8gde6XoN8AAAAA:4NWB0pHcy38dGbVogF2SakdNr1VesDTEqpFVBMxHg2mRwQwnXhmidEnhR4tjn9UTCbzoU_tbIKyhvQ.
- Forrest W. Crawford, Jiacheng Wu, and Robert
Heimer, “Hidden Population Size Estimation from Respondent-Driven
Sampling: A Network Approach,” Journal of the American
Statistical Association (2018): 1–12, https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1285775?casa_token=j3VcQGcoB-sAAAAA%3A431uDwCZPR_ZqK7nlEhPpu53_MxHL0tdkSWbv_omMC_-VDiya6N9OakKCfJrZYTHVmYN2o70WgDTmg.
- Juanjuan Zhang et al., “Changes in
Contact Patterns Shape the Dynamics of the COVID-19
Outbreak in China,” Science 368, no. 6498
(June 2020): 1481–1486, doi:10.1126/science.abb8001.
- Jacco Wallinga, Peter Teunis, and Mirjam
Kretzschmar, “Using Data on Social
Contacts to Estimate Age-specific
Transmission Parameters for Respiratory-spread Infectious Agents,”
American Journal of Epidemiology 164, no. 10 (November 2006):
Network models, connectivity, and small worlds
Thu, Sep 8 - Network models, connectivity, and small
Readings to discuss:
- Stanley Milgram, “The Small World
Problem,” Psychology Today 1 (1967): 62–67, https://courses.cit.cornell.edu/info2950_2012sp/milgram.pdf.
- J. Travers and S. Milgram, “An
Experimental Study of the Small World Problem,”
Sociometry (1969): 425–443, http://www.jstor.org/stable/10.2307/2786545.
- D. J. Watts and S. H. Strogatz,
“Collective Dynamics of ‘Small-World’networks,”
Nature 393, no. 6684 (1998): 440–442, http://www.nature.com/nature/journal/v393/n6684/abs/393440a0.html.
- Jon M. Kleinberg, “Navigation in a
Small World,” Nature 406, no. 6798 (2000): 845–845, http://www.nature.com/nature/journal/v406/n6798/abs/406845a0.html.
- Duncan J. Watts, Peter Sheridan Dodds, and
Mark EJ Newman, “Identity and Search in Social Networks,”
Science 296, no. 5571 (2002): 1302–1305, http://science.sciencemag.org/content/296/5571/1302.short.
- P. S. Dodds, R. Muhamad, and D. J. Watts,
“An Experimental Study of Search in Global Social
Networks,” Science 301, no. 5634 (2003): 827, Äì–829, http://www.sciencemag.org/content/301/5634/827.short.
Some fairly recent online discussion of the small world
Background and related:
- D. J. Watts, Six Degrees:
The Science of a Connected Age (WW Norton &
Company, 2003), ch. 1-3.
- Seth A. Marvel et al., “The Small-World
Effect Is a Modern Phenomenon,” arXiv Preprint
arXiv:1310.2636 (2013), http://arxiv.org/abs/1310.2636.
- Brian Uzzi and Jarrett Spiro,
“Collaboration and Creativity: The Small World
Problem,” American Journal of Sociology
111, no. 2 (2005): 447–504, http://www.jstor.org/stable/10.1086/432782.
- David Liben-Nowell et al., “Geographic
Routing in Social Networks,” Proceedings of the National
Academy of Sciences of the United States of America 102, no. 33
(2005): 11623–11628, http://www.pnas.org/content/102/33/11623.short.
- Mark Granovetter, “Ignorance,
Knowledge, and Outcomes in a Small World,” Science 301,
no. 5634 (2003): 773–774, http://science.sciencemag.org/content/301/5634/773.short.
Structure and segregation
Thu, Sep 29
- Peter M. Blau, “A Macrosociological
Theory of Social Structure,” American Journal of
Sociology 83, no. 1 (1977): 26–54, http://www.journals.uchicago.edu/doi/abs/10.1086/226505.
- Jure Leskovec, Daniel Huttenlocher, and Jon
Kleinberg, “Signed Networks in Social Media,” in
Proceedings of the SIGCHI Conference on Human Factors
in Computing Systems (ACM, 2010), 1361–1370, https://dl.acm.org/citation.cfm?id=1753532.
- Thomas A. DiPrete et al., “Segregation
in Social Networks Based on Acquaintanceship
and Trust,” American Journal of Sociology
116, no. 4 (2011): 1234–83, http://www.jstor.org/stable/10.1086/659100.
- Mark EJ Newman and Michelle Girvan,
“Finding and Evaluating Community Structure in Networks,”
Physical Review E 69, no. 2 (2004): 026113, http://journals.aps.org/pre/abstract/10.1103/PhysRevE.69.026113.
Also interesting (but we won’t have time to discuss in class):
- M. Girvan and M. E. J. Newman,
“Community Structure in Social and Biological Networks,”
Proceedings of the National Academy of Sciences 99, no. 12
(2002): 7821, http://www.pnas.org/content/99/12/7821.short.
- Amir Goldberg, “Mapping Shared
Understandings Using Relational Class Analysis: The Case of the Cultural
Omnivore Reexamined,” American Journal of Sociology 116,
no. 5 (2011): 1397–1436, http://www.jstor.org/stable/10.1086/657976.
- Brian Karrer and Mark EJ Newman,
“Stochastic Blockmodels and Community Structure in
Networks,” Physical Review E 83, no. 1 (2011): 016107,
- R. L. Breiger, “The Duality of Persons
and Groups,” Social Forces 53, no. 2 (1974): 181—190, http://sf.oxfordjournals.org/content/53/2/181.short.
- Laura Katherine Gee, Jason J. Jones, and
Moira Burke, “Social Networks and Labor
Markets: How Strong Ties Relate to Job Finding
On Facebook’s Social Network” (2016), http://www.journals.uchicago.edu/doi/pdfplus/10.1086/686225.
- Peter J. Bickel and Aiyou Chen, “A
Nonparametric View of Network Models and Newman and Other
Modularities,” Proceedings of the National Academy of
Sciences 106, no. 50 (2009): 21068–21073, http://www.pnas.org/content/106/50/21068.full.
- M. Granovetter, “Economic Action and
Social Structure: The Problem of Embeddedness,” Readings in
Economic Sociology (1985): 63–68, http://onlinelibrary.wiley.com/doi/10.1002/9780470755679.ch5/summary.
- Alexander Isakov et al., “The
Structure of Negative Social Ties in
Rural Village Networks,” Sociological
Science 6 (March 2019): 197–218, doi:10.15195/v6.a8.
Thu, Oct 6 - Homophily - network formation based on
- Gueorgi Kossinets and Duncan J. Watts,
“Empirical Analysis of an Evolving Social
Network,” Science 311, no. 5757 (January 2006):
- 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:
- the theory/background section of this paper is an excellent review
of homophily: A. Wimmer and K. Lewis,
“Beyond and Below Racial Homophily: ERG
Models of a Friendship Network Documented on
Facebook1,” American Journal of Sociology
116, no. 2 (2010): 583–642, http://www.jstor.org/stable/10.1086/653658.
- 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.
- Matthew O. Jackson and Brian W. Rogers,
“Meeting Strangers and Friends of Friends: How Random
Are Social Networks?” The American Economic Review 97,
no. 3 (2007): 890–915, http://www.ingentaconnect.com/content/aea/aer/2007/00000097/00000003/art00015.
- Jukka-Pekka Onnela et al., “Geographic
Constraints on Social Network Groups,” PLoS One 6, no. 4
(2011): e16939, http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0016939.
- Yosh Halberstam and Brian Knight,
“Homophily, Group Size, and the Diffusion of Political Information
in Social Networks: Evidence from
Twitter,” Journal of Public Economics 143
(November 2016): 73–88, doi:10.1016/j.jpubeco.2016.08.011.
Thu, Oct 13 - Network formation over time
- A. L. Barabási and R. Albert,
“Emergence of Scaling in Random Networks,” Science
286, no. 5439 (1999): 509—512, http://www.sciencemag.org/content/286/5439/509.short.
- Anna D. Broido and Aaron Clauset,
“Scale-Free Networks Are Rare,” arXiv:1801.03400
[Physics, q-Bio, Stat] (January 2018), http://arxiv.org/abs/1801.03400.
- Peter S. Bearman, James Moody, and Katherine
Stovel, “Chains of Affection: The Structure of
Adolescent Romantic and Sexual Networks1,” American Journal
of Sociology 110, no. 1 (2004): 44–91, http://www.jstor.org/stable/10.1086/386272.
- M. Morris and M. Kretzschmar,
“Concurrent Partnerships and the Spread of
HIV,” AIDS 11, no. 5 (1997): 641, http://journals.lww.com/aidsonline/Abstract/1997/05000/Concurrent_partnerships_and_the_spread_of_HIV.12.aspx.
Some recent online discussions of the power law debate (not required
Also interesting (but we won’t have time to discuss in class):
- The online textbook Network Science by
- A. Clauset, C. R. Shalizi, and M. E. J.
Newman, “Power-Law Distributions in Empirical Data,”
arXiv:0706.1062 (2007), http://epubs.siam.org/doi/abs/10.1137/070710111.
- Tuan Q. Phan and Edoardo M. Airoldi, “A
Natural Experiment of Social Network Formation and Dynamics,”
Proceedings of the National Academy of Sciences 112, no. 21
(2015): 6595–6600, http://www.pnas.org/content/112/21/6595.short.
- Fredrik Liljeros et al., “The Web of
Human Sexual Contacts,” Nature 411, no. 6840 (2001):
- Abigail Z. Jacobs et al., “Assembling
Thefacebook: Using Heterogeneity to Understand Online
Social Network Assembly,” in Proceedings of the ACM Web
Science Conference (ACM, 2015), 18, http://dl.acm.org/citation.cfm?id=2786477.
- Mark EJ Newman, “Coauthorship Networks
and Patterns of Scientific Collaboration,” Proceedings of the
National Academy of Sciences 101, no. suppl 1 (2004): 5200–5205, http://www.pnas.org/content/101/suppl_1/5200.short.
- Mirjam Kretzschmar, Richard G. White, and
Michel Caraël, “Concurrency Is More Complex Than It Seems,”
AIDS (London, England) 24, no. 2 (2010): 313, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887386/.
(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):
- 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:
- 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.
- Shweta Bansal, Bryan T. Grenfell, and Lauren
Ancel Meyers, “When Individual Behaviour Matters: Homogeneous and
Network Models in Epidemiology,” Journal of the Royal Society
Interface 4, no. 16 (2007): 879–891, http://rsif.royalsocietypublishing.org/content/4/16/879.short.
- Akihiro Nishi et al., “Network
Interventions for Managing the COVID-19 Pandemic and
Sustaining Economy,” Proceedings of the National Academy of
Sciences 117, no. 48 (December 2020): 30285–30294, doi:10.1073/pnas.2014297117.
- Matt J. Keeling et al., “Individual
Identity and Movement Networks for Disease Metapopulations,”
Proceedings of the National Academy of Sciences 107, no. 19
(2010): 8866–8870, http://www.pnas.org/content/107/19/8866.short.
- ABHIJIT BANERJEE et al., “Using Gossips
to Spread Information: Theory and Evidence from a Randomized Controlled
Trial” (2017), https://arxiv.org/pdf/1406.2293.pdf.
- Pejman Rohani, Xue Zhong, and Aaron A. King,
“Contact Network Structure Explains the Changing Epidemiology of
Pertussis,” Science 330, no. 6006 (2010): 982–985, http://science.sciencemag.org/content/330/6006/982.short.
- Marcel Salathé and James H. Jones,
“Dynamics and Control of Diseases in Networks with Community
Structure,” PLoS Comput Biol 6, no. 4 (2010): e1000736,
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,
- 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,
- 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
- David W. Nickerson, “Is Voting
Contagious? Evidence from Two Field Experiments,”
American Political Science Review 102, no. 1 (2008): 49–57, http://journals.cambridge.org/abstract_S0003055408080039.
- Robert M. Bond et al., “A
61-Million-Person Experiment in Social Influence and Political
Mobilization,” Nature 489, no. 7415 (2012): 295–298, http://www.nature.com/nature/journal/v489/n7415/abs/nature11421.html.
- Abhijit Banerjee et al., “The Diffusion
of Microfinance,” Science 341, no. 6144 (2013): 1236498,
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,
- 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),
- 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,
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
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):
Also interesting, but we will not have time to discuss:
over the C-F findings on the contagion of obesity (blog post by
- Russell Lyons, “The Spread of
Evidence-Poor Medicine via Flawed Social-Network Analysis,”
Statistics, Politics, and Policy 2, no. 1 (2011), http://www.degruyter.com/view/j/spp.2011.2.issue-1/spp.2011.2.1.1024/spp.2011.2.1.1024.xml.
Contagion Theory: Examining Dynamic Social Networks and Human
Behavior (a response to some criticisms of Christakis and
- Charles F. Manski, “Identification of
Endogenous Social Effects: The Reflection Problem,”
The Review of Economic Studies 60, no. 3 (1993): 531–542, http://restud.oxfordjournals.org/content/60/3/531.short.
For the mini-conference, we will each give a brief presentation of
our paper. There’s no specific reading for this week.
- Duncan J. Watts, “The ’New’ Science of
Networks,” Annual Review of Sociology (2004): 243–270,
- Mark EJ Newman and Juyong Park, “Why
Social Networks Are Different from Other Types of Networks,”
Physical Review E 68, no. 3 (2003): 036122, http://journals.aps.org/pre/abstract/10.1103/PhysRevE.68.036122.
- Matthew O. Jackson, Brian W. Rogers, and Yves
Zenou, “The Economic Consequences of Social Network
Structure” (2016), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2467812.
- 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,
- 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,
- Delia Baldassarri and Peter Bearman,
“Dynamics of Political Polarization,”
American Sociological Review 72, no. 5 (October 2007): 784–811,
Collaboration and cooperation
- Coren L. Apicella et al., “Social
Networks and Cooperation in Hunter-Gatherers,” Nature
481, no. 7382 (2012): 497–501, http://www.nature.com/nature/journal/v481/n7382/full/nature10736.html%3FWT.ec_id%3DNATURE-20120126.
- Jing Wang, Siddharth Suri, and Duncan J.
Watts, “Cooperation and Assortativity with Dynamic Partner
Updating,” Proceedings of the National Academy of
Sciences 109, no. 36 (2012): 14363–14368, http://www.pnas.org/content/109/36/14363.short.
- David G. Rand et al., “Static Network
Structure Can Stabilize Human Cooperation,” Proceedings of
the National Academy of Sciences 111, no. 48 (2014): 17093–17098,
- Akihiro Nishi et al., “Inequality and
Visibility of Wealth in Experimental Social Networks,”
Nature 526, no. 7573 (2015): 426–429, http://www.nature.com/nature/journal/v526/n7573/abs/nature15392.html.
- Roger Guimera et al., “Team Assembly
Mechanisms Determine Collaboration Network Structure and Team
Performance,” Science 308, no. 5722 (2005): 697–702, http://science.sciencemag.org/content/308/5722/697.short.
- Winter Mason and Duncan J. Watts,
“Collaborative Learning in Networks,” Proceedings of
the National Academy of Sciences 109, no. 3 (2012): 764–769, http://www.pnas.org/content/109/3/764.short.
- Winter Mason, Siddharth Suri, and Duncan J.
Watts, “Long-Run Learning in Games of Cooperation,” in
Proceedings of the Fifteenth ACM Conference on
Economics and Computation (ACM, 2014),
- Matthew O. Jackson, Tomas
Rodriguez-Barraquer, and Xu Tan, “Social Capital and Social
Quilts: Network Patterns of Favor Exchange,” The
American Economic Review 102, no. 5 (2012): 1857–1897, http://www.ingentaconnect.com/content/aea/aer/2012/00000102/00000005/art00004.
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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
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These are some basic expectations of students with regards to
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should not have been submitted for credit in another course unless you
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