Professor Dennis Feehan
Office: 2232 Piedmont Ave, Room 210
Email: my last name at berkeley.edu
Class meetings Tu/Th 11am-12.30pm, 141 McCone
Class number: 41045
Office hours: Tu 3-4pm, 210 Dept. of Demography
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||Personal networks; social connectedness and social isolation in America; survey data collection|
|Homophily / Tie strength I||2||1/23||Basic graph theory: definitions, types of networks, types of network data||Homework 1|
|1/25||Strength of weak ties; social capital|
|Homophily / Tie strength II||3||1/30||Networks in context; homophily||Personal networks lecture notebook|
|2/1||Positive and negative relationships|
|Affiliation networks||4||2/6||Working with entire network data; quantifying network structure: degree distributions, average path length, clustering||Working with whole network data notebook|
|2/8||Affiliation networks; foci; group membership; one-mode projections of bipartite networks||Structural balance notebook|
|Network models||5||2/13||Intro to mathematical network models; the Erdos-Renyi model and its predictions||Erdos-Renyi random networks notebook|
|2/15||Intro to mathematical network models; the Erdos-Renyi model and its predictions||Homework 2|
|Small worlds||6||2/20||Small worlds|
|2/22||Search in small worlds / Scale-free networks|
|Search in small worlds, scale-free networks||8||3/6||Midterm recap; finish search in small worlds|
|3/8||Scale-free networks||Homework 3|
|Simple contagion||9||3/13||Diseases and simple contagion in general; SIR model|
|3/15||SIR model on networks||Centrality and SIR model notebook
|10||3/20||Centrality, influence, and network disease models||Homework 4|
|3/22||Sexual networks, concurrency, and HIV|
|Complex contagion||12||4/3||Complex contagion; social influence, decisions, threshold models|
|13||4/10||Can your friends make you fat? Experimental and observational studies of complex contagion|
|Cooperation and collaboration||15||4/24||Cooperation; the prisoner’s dilemma and public goods games|
|4/26||Experimental evidence for the emergence of cooperation|
|Online social networks||Online social networks; online/offline relationships; digital divide|
|Filter bubbles; going viral|
|Network structure / communities||More complex network models: the block model, the configuration model|
|Community detection; models of network formation|
|Strong and weak tie networks||Family and kinship networks; the grandmother hypothesis|
|Weak tie networks|
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.
PDFs of each of the readings will be posted on the bCourses site.
There will be a total of 5 to 7 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
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.
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
Rainie, L., & Wellman, B. (2012). Networked: The new social operating system. Mit Press. https://books.google.com/books?hl=en\&lr=\&id=bYJGna0AhdAC\&oi=fnd\&pg=PR7\&dq=wellman+networked\&ots=ZJCHcKjHyQ\&sig=q1F5tTOqLpcJ4WqFYlwHj-dkhwg
Watts, D. J. (2003). Six degrees: The science of a connected age. WW Norton & Company.