NB: this syllabus will change - please check back regularly
Professor Dennis Feehan
Office: 2232 Piedmont Ave, Room 210
Email: my last name at berkeley.edu
Class meetings Tu/Th 9:30am-11:00am, 141 McCone
Class number: 41045
Office hours: Tu 3-4pm, 210 Dept. of Demography
Final exam: Wednesday, May 15th, 11:30am-2:30pm (141 McCone)
Piazza page
Bcourses page
Lecture slides
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.
| Theme | Week | Date | Topic | Lecture | Lab | Hwk |
|---|---|---|---|---|---|---|
| Intro | 1 | 1/22 | Intro / what social networks are / class info | Lab 0 | ||
| 1/24 | Basic graph theory: definitions, types of networks, types of network data; survey data collection | Hwk 1 | ||||
| Personal networks | 2 | 1/29 | Personal networks; social connectedness and social isolation in America | |||
| 1/31 | Working with personal network data; our survey results | Demo 1 | ||||
| Complete network data | 3 | 2/5 | Working with entire network data; quantifying network structure | Demo 2 | Lab 1 | Hwk 2 |
| Network models: the ER model | 2/7 | Intro to mathematical network models; the Erdos-Renyi model and its predictions | Demo 3 | |||
| Homophily / Tie strength | 4 | 2/12 | Strength of weak ties; social capital | Lab 2 | ||
| 2/14 | Networks in context; homophily | Demo 4 | Hwk 3 | |||
| Balance theory | 5 | 2/19 | Positive and negative relationships | Demo 5 | Lab 3 | |
| Affiliation networks and foci | 2/21 | Affiliation networks; foci; group membership; one-mode projections of bipartite networks | Demo 6 | |||
| Small worlds | 6 | 2/26 | Small worlds | Hwk 4 | ||
| 2/28 | Midterm review | |||||
| 7 | 3/5 | Midterm | ||||
| Search in small worlds, scale-free networks | 3/7 | Midterm recap; search in small worlds | ||||
| 8 | 3/12 | Scale-free networks | BA model | Hwk 5 | ||
| Simple contagion | 3/14 | Diseases and simple contagion in general; SIR model | SIR demo | |||
| 9 | 3/19 | SIR model on networks | network SIR demo | Lab 4 | ||
| 3/21 | Centrality, influence, and network disease models | threshold infectiousness demo | ||||
| 10 | 3/26 | SPRING BREAK | ||||
| 3/28 | SPRING BREAK | |||||
| 11 | 4/2 | NO CLASS | Lab 5 | |||
| Concurrency | 4/4 | Sexual networks, concurrency, and HIV | Concurrency demo | |||
| Cooperation | 12 | 4/9 | Cooperation | Axelrod-style tournament demo | ||
| 4/11 | Guest speaker - Cooperation and collaboration | |||||
| Social influence | 13 | 4/16 | Social influence, herding, and cascades | Hwk 6 | ||
| Complex contagion | 4/18 | Threshold models and complex contagion | ||||
| 14 | 4/23 | Complex contagion on networks | ||||
| 4/25 | Complex contagion on networks, cont. | |||||
| Empirical studies of contagion | 15 | 4/30 | Can your friends make you fat? Experimental and observational studies of complex contagion | |||
| 5/2 | Wrap up | |||||
| 5/7 | READING WEEK | |||||
| 5/9 | READING WEEK | |||||
| 5/15 | Final exam! 11:30am-2:30pm (Location TBD) |
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 about 9 labs, each based on a series of exercises in a Jupyter notebook. The labs are intended to give you a chance to explore how the concepts we cover in lecture can be applied to the analysis of real and simulated datasets. The labs are also intended to help prepare you for the homework assignments. You will turn labs in electronically; they will be graded for effort, but not correctness.
There will be a total of 5 to 7 homework assignments. You can drop the homework with the lowest score. 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 7, 2019 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 (May 15, at 11:30am). The final exam will be cumulative.
I will post a small number (4-5) 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.
| Component | % of grade |
|---|---|
| 6 Homeworks (you can drop your lowest score) | 30 |
| 5 Labs (you can drop your lowest score) | 20 |
| 4 Quizzes | 5 |
| Mid-term exam | 20 |
| Final exam | 25 |
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Axelrod, R. M. (1984). The evolution of cooperation. New York : Basic Books, c1984.
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.
Lurie, M. N., & Rosenthal, S. (2010). Concurrent partnerships as a driver of the HIV epidemic in sub-Saharan Africa? The evidence is limited. AIDS and Behavior, 14(1), 17–24.
Watts, D. J. (2003). Six degrees: The science of a connected age. WW Norton & Company.