Class meetings: Tuesdays and Thursdays, 2:00-3:30PM, 60
Social Science Building
Office hours: (See Ed post)
Email: feehan [at] berkeley.edu
Web: https://www.dennisfeehan.org/teaching/2022fa_demog180.html
Ed page: https://edstem.org/us/courses/25558/discussion/
Gradescope page: https://www.gradescope.com/courses/422001
Bcourses page: https://bcourses.berkeley.edu/courses/1518182
Lecture slides: https://drive.google.com/drive/folders/1Wo5TVXSiWILle2I3fDHEL9XfVffmvy-8
Final exam: TBA (Exam Location TBD)
(Syllabus last updated: 2022-November-10)
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.
Please re-check the syllabus frequently; it will be updated as the semester progresses
| Week | Date | Topic | Lecture | Lab | Hwk |
|---|---|---|---|---|---|
| 1 | Thu, Aug 25 | Intro / what social networks are / class info | |||
| 2 | Tue, Aug 30 | Basic graph theory: definitions, types of networks, types of network data; survey data collection | Lab 0 | Hwk 1 | |
| 2 | Thu, Sep 1 | Personal networks; social connectedness and social isolation in America | |||
| 3 | Tue, Sep 6 | Working with personal network data; our survey results | personal networks demo | Lab 1_1 Lab 1_2 | |
| 3 | Thu, Sep 8 | Working with entire network data; quantifying network structure | whole network demo | Hwk 2 | |
| 4 | Tue, Sep 13 | Intro to mathematical network models; the Erdos-Renyi model and its predictions | ER random networks demo | Lab 2 | |
| 4 | Thu, Sep 15 | Strength of weak ties; social capital | Triadic closure in an email network | ||
| 5 | Tue, Sep 20 | Networks in context; homophily | Strength of weak ties demo | Lab 3 | Hwk 3 |
| 5 | Thu, Sep 22 | Positive and negative relationships | Structural balance demo | ||
| 6 | Tue, Sep 27 | Affiliation networks; foci; group membership; one-mode projections of bipartite networks | Lab 4 | Hwk 4 | |
| 6 | Thu, Sep 29 | Small worlds | |||
| 7 | Tue, Oct 4 | Search in small worlds | Lab 5 | Hwk 5 | |
| 7 | Thu, Oct 6 | Midterm review | Vote on midterm review topics | ||
| 8 | Tue, Oct 11 | Midterm | |||
| 8 | Thu, Oct 13 | Scale-free networks | BA model | ||
| 9 | Tue, Oct 18 | Empirical studies of network structure | |||
| 9 | Thu, Oct 20 | Diseases and simple contagion in general; SIR model | SIR demo | ||
| 10 | Tue, Oct 25 | SIR model on networks | threshold infectiousness demo | Lab 6 | |
| 10 | Thu, Oct 27 | Centrality, influence, and network disease models | Hwk 6 | ||
| 11 | Tue, Nov 1 | Guest speaker - Casey Breen | Quiz 1 - guest lecture | ||
| 11 | Thu, Nov 3 | Sexual networks, concurrency, and HIV | |||
| 12 | Tue, Nov 8 | Social influence, herding, and cascades | Quiz 2 - concurrency | ||
| 12 | Thu, Nov 10 | Threshold models and complex contagion | Hwk 7 | ||
| 13 | Tue, Nov 15 | Complex contagion on networks | Quiz 3 - the friendship paradox | ||
| 13 | Thu, Nov 17 | Complex contagion on networks, cont. | |||
| 14 | Tue, Nov 22 | NO CLASS | |||
| 14 | Thu, Nov 24 | THANKSGIVING (NO CLASS) | |||
| 15 | Tue, Nov 29 | Is obesity contagious? Experimental and observational studies of complex contagion | Quiz 4 - is obesity contagious? | ||
| 15 | Thu, Dec 1 | Wrap up | |||
| 16 | Tue, Dec 6 | READING WEEK | |||
| 16 | Thu, Dec 8 | READING WEEK |
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:
We will also read chapters from popular science books written by leading network researchers, including selections from
Finally, we will read 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. Whenever possible, PDFs of the readings will be posted on the bCourses site.
There will be a total of 6 to 8 homework assignments and a similar number of labs. The homeworks and labs 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. Most homeworks and labs 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.
Labs are graded based on effort; therefore, you can get full credit on a lab even if you do not get all of the answers right. Labs must be handed in on time for full credit.
Homeworks are graded on correctness and must be handed in on time for full credit. However, we will drop the homework with the lowest score; thus, you can miss handing in one homework over the course of the semester without it affecting your grade.
There will be two in-class closed book examinations. The mid-term examination will be held during normal class time in our normal classroom; the timing of this midterm will be designed to assess your mastery of the core concepts in social networks. The final will be held during the final exam period (see the date/time above). The final exam will be cumulative.
We will post a small number (2-4) quizzes on bCourses over the semester. These quizzes will consist of 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 (including guest lectures).
| Component | % of grade |
|---|---|
| Homeworks (you can drop your lowest score) | 30 |
| Labs | 20 |
| Mid-term exam | 20 |
| Final exam | 25 |
| Quizzes | 5 |
Intro to social networks; course overview
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Personal networks; social connectedness and social isolation in America; survey data collection
Sampling variation and the bootstrap; null models and a permutation test
Patterns of homophily in Berkeley students’ personal networks
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Classroom Climate
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Academic Integrity
The high academic standard at the University of California, Berkeley, 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 work reflects unique ideas or properly attributes the ideas to the original sources.
These are some basic expectations of students with regards to academic integrity: