Professor Dennis Feehan
Office: 2232 Piedmont Ave, Room 210
Email: my last name at berkeley.edu
Class meetings: TBD
Office hours: TBD
The science of social networks focuses on measuring, modeling, and understanding the different ways that people are connected to one another. In this class, we will use a broad toolkit of theories and methods drawn from the social, natural, and mathematical sciences to learn what a social network is, to understand how to work with social network data, and to illustrate some of the ways that social networks can be useful in theory and in practice. We will see that network ideas are powerful enough to be used everywhere from CDC and UNAIDS, where network models help epidemiologists prevent the spread of HIV, to Silicon Valley, where data scientists use network ideas to build products that enable people all across the globe to connect with one another.
|Intro||1||1/16||Intro / what social networks are / class info|
|1/18||Definitions, types of networks, types of network data, discussion about survey research|
|Homophily / Tie strength I||2||1/23||Social connectedness and social isolation in America; survey data collection|
|1/25||Social capital and the strength of weak ties; patterns of tie strength in students’ survey results|
|Homophily / Tie strength II||3||1/30||Homophily; choice and structure; social implications; patterns of homophily in students’ survey results|
|2/1||Working with entire network data; quantifying network structure: degree distributions, average path length, clustering|
|Network models||4||2/6||Intro to mathematical network models; the Erdos-Renyi model and its predictions|
|2/8||Using the ER model to analyze empirical data; homophily example|
|Small worlds||5||2/13||The small world phenomenon|
|2/15||Search in small worlds; scale-free networks|
|Affiliation networks||6||2/20||Affiliation networks; foci; group membership; one-mode projections of bipartite networks|
|2/22||Affiliation networks cont.|
|Simple contagion||8||3/6||SIR model, types of centrality|
|3/8||Why your friends have more friends than you do; network structure and disease|
|9||3/13||Sexual networks, concurrency, and HIV|
|Complex contagion||3/15||Complex contagion; social influence, decisions, threshold models|
|10||3/20||Experimental studies of complex contagion|
|3/22||Can your friends make you fat? Observational studies of network contagion|
|Online social networks||12||4/3||Online social networks; online/offline relationships; digital divide|
|4/5||Filter bubbles; going viral|
|Network structure / communities||13||4/10||More complex network models: the block model, the configuration model|
|4/12||Community detection; models of network formation|
|Strong and weak tie networks||14||4/17||Family and kinship networks; the grandmother hypothesis|
|4/19||Weak tie networks|
|Cooperation and collaboration||15||4/24||Cooperation; the prisoner’s dilemma and public goods games|
|4/26||Experimental evidence for the emergence of cooperation|
Lectures will introduce and develop key theoretical and technical concepts in the study of social networks. To illustrate these ideas, some of the lectures will have a live lab component, where we will interactively discuss and work through an analysis in a Jupyter notebook. These live labs will help us explore and develop intuition about key concepts in the course.
The lectures are organized so that the first set of material, up to the mid-term exam, is a survey of the core theories, concepts, and methods needed to be familiar with social networks. After the mid-term, the lectures will turn to an exploration of how these core ideas have been used, modified, and deepened in several different topic areas.
You are responsible for all of the material covered in lectures, as well as any announcements made there.
The course readings will include selections from the textbook Networks, Crowds, and Markets by Easley and Kleinberg; chapters from popular science books written by leading network researchers, and several journal and newspaper articles.
The readings serve two purposes: (1) they provide an introduction and reference for key concepts that we will need to study social networks; (2) they illustrate how social network ideas get used in real world research and applications across many different disciplines. You are expected to do the reading before each class.
The course reader will be available from TBD. I have also posted PDFs of each of the readings on the bCourses site.
There will be a total of 9 homework assignments. You can drop the homework with the lowest score, meaning that 8 of the homeworks count; together, they are worth 40% of your final grade. These homeworks are a critical part of the learning you will do in this class: they give you an opportunity to explore the topics we cover in the readings and in lecture on your own. They also give you a chance to practice your writing and your data analysis and programming skills. The format for each homework will ask you to provide some written arguments and to solve some problems by writing Python code in a Jupyter notebook. It can be helpful and educational to discuss the assignments with other students in the class, but (1) all of the work should be your own (i.e., you are not allowed to just copy code, answers, or arguments); (2) you should make a note of the names of the other students you worked with when handing your assignments in.
There will be two in-class closed book examinations. The mid-term examination will be held on March 1, 2018 during normal class time in our normal classroom; the timing of this midterm is designed to assess your mastery of the core concepts in social networks. The final will be held during the final exam period (exact date/time TBD). The final exam will be cumulative.
I will post five quizzes on bCourses over the semester. These quizzes will be 5-10 multiple choice questions; the goal of these quizzes will be to ensure that you are staying up to date with the reading and lecture materials covered in the class. The quizzes will each be worth 2% of your grade, for a total of 10%.
|Component||% of grade|
Intro to mathematical network models; the Erdos-Renyi model and its predictions
Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132. http://science.sciencemag.org/content/348/6239/1130.short
Barash, D. P. (2016, May). What Good Is Grandma? Nautilus. http://nautil.us/issue/36/aging/what-good-is-grandma. Accessed 13 August 2017
Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press. https://www.cs.cornell.edu/home/kleinber/networks-book/
Epstein, H. (2008). The invisible cure: Why we are losing the fight against AIDS in Africa. Macmillan.
Feehan, D. M., Umubyeyi, A., Mahy, M., Hladik, W., & Salganik, M. J. (2016). Quantity Versus Quality: A Survey Experiment to Improve the Network Scale-up Method. American Journal of Epidemiology, kwv287. http://aje.oxfordjournals.org/content/early/2016/03/24/aje.kwv287. Accessed 28 March 2016
Feld, S. L. (1991). Why your friends have more friends than you do. American Journal of Sociology, 1464–1477. http://www.jstor.org/stable/2781907. Accessed 11 February 2016
Goel, S., Anderson, A., Hofman, J., & Watts, D. J. (2015). The structural virality of online diffusion. Management Science, 62(1), 180–196. http://pubsonline.informs.org/doi/abs/10.1287/mnsc.2015.2158
Nishi, A., Shirado, H., Rand, D. G., & Christakis, N. A. (2015). Inequality and visibility of wealth in experimental social networks. Nature, 526(7573), 426–429. http://www.nature.com/nature/journal/v526/n7573/abs/nature15392.html. Accessed 16 March 2017
Watts, D. J. (2003). Six degrees: The science of a connected age. WW Norton & Company. Accessed 26 June 2012