Theme | Week | Date | Topic | Demo | Lab | Hwk |
---|---|---|---|---|---|---|
1 |
Thu, Aug 29 | Intro / what social networks are / basic graph theory / class info | Lab 0: Getting started w/ Jupyter notebook / test submitting a lab |
|||
2 |
Tue, Sep 3 | Personal networks; social connectedness and social isolation in America | ||||
2 |
Thu, Sep 5 | Triadic closure | ||||
3 |
Tue, Sep 10 | Strength of Weak Ties, Social Capital, Structural Holes | ||||
3 |
Thu, Sep 12 | Network centrality / the Friendship Paradox | ||||
4 |
Tue, Sep 17 | Networks in context; homophily; affiliation networks; and foci | Lab 3: Enriching network data and testing a hypothesis about homophily |
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4 |
Thu, Sep 19 | Positive and negative networks | ||||
5 |
Tue, Sep 24 | Intro to mathematical network models; the Erdos-Renyi model and its predictions | ||||
5 |
Thu, Sep 26 | Small worlds | ||||
6 |
Tue, Oct 1 | Search in small worlds | ||||
6 |
Thu, Oct 3 | Scale-free networks | ||||
7 |
Tue, Oct 8 | Midterm review | ||||
7 |
Thu, Oct 10 | Midterm | ||||
8 |
Tue, Oct 15 | More models: configuration model and stochastic block model | ||||
8 |
Thu, Oct 17 | Community detection | ||||
9 |
Tue, Oct 22 | Empirical studies of network structure | ||||
9 |
Thu, Oct 24 | Diseases and simple contagion in general; SIR model | ||||
10 |
Tue, Oct 29 | SIR model on networks; centrality, influence and network disease models | ||||
10 |
Thu, Oct 31 | Sexual networks, concurrency, and HIV | ||||
11 |
Tue, Nov 5 | Empirical studies of simple contagion | ||||
11 |
Thu, Nov 7 | Social influence, herding, and cascades | ||||
12 |
Tue, Nov 12 | Threshold models and complex contagion | ||||
12 |
Thu, Nov 14 | Complex contagion on networks | ||||
13 |
Tue, Nov 19 | Complex contagion on networks, cont. | ||||
13 |
Thu, Nov 21 | TBD | ||||
14 |
Tue, Nov 26 | NO CLASS | ||||
14 |
Thu, Nov 28 | THANKSGIVING (NO CLASS) | ||||
15 |
Tue, Dec 3 | Is obesity contagious? Experimental and observational studies of complex contagion | ||||
15 |
Thu, Dec 5 | Wrap up | ||||
16 |
Tue, Dec 10 | READING WEEK |
Social Networks (Demography 180)
(Syllabus last updated: 2024-October-01)
Quick links
Class meetings: Tuesdays and Thursdays, 12:30-2:00PM, 166 Social Science Building
Web: https://www.dennisfeehan.org/demog180-fa2024
Ed page: https://edstem.org/us/courses/62283/discussion/
Gradescope page: https://www.gradescope.com/courses/823074
Bcourses page: https://bcourses.berkeley.edu/courses/1537401
Lecture slides: https://drive.google.com/drive/u/0/folders/1Hd7PD84a60r11dcRhMQx4axd0A-vdrRN
Final exam: TBA
Staff
Professor Dennis Feehan, feehan [at] berkeley.edu
Office hours: (see Ed post)
GSIs, TBA
Overview
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
Requirements
Lectures
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.
Required readings
The course readings will include selections from the textbook Networks, Crowds, and Markets by Easley and Kleinberg:
- David Easley and Jon Kleinberg Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Cambridge University Press, 2010), https://www.cs.cornell.edu/home/kleinber/networks-book/.
We will also read chapters from popular science books written by leading network researchers, including selections from
- D. J. Watts Six Degrees: The Science of a Connected Age (WW Norton & Company, 2003).
- Helen Epstein The Invisible Cure: Why We Are Losing the Fight Against AIDS in Africa (Macmillan, 2008).
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.
Homeworks and labs
There will be a total of 6 to 8 homeworks, a similar number of labs, and one mini-project. These assignments 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.
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 and 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. (Note that you cannot drop the grade for the mini-project.)
The mini-project is like an extended homework that comes after all of the notebook-based homeworks. The goal is to give you a chance to start from scratch with a new network dataset and to demonstrate that you can perform an analysis on the network with minimal hand-holding. It will count as two homeworks.
Collaboration: 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. Please treat AI tools like ChatGPT like another student: follow rules (1) and (2); that is, don’t copy code or text directly from an AI tool and please make a note of any tool you consulted at the top of your assignment.
Exams
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.
Participation and quizzes
In some lectures, you will be asked to participate in discussions and interactive demonstrations. There will also be a small number (about 2) quizzes on bCourses over the semester. These quizzes will consist of a few 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).
Summary
Component | % of grade |
---|---|
Homeworks (you can drop your lowest score) | 35 |
Labs | 15 |
Mid-term exam | 15 |
Final exam | 30 |
Participation + Quizzes | 5 |
Detailed modules
Introduction to social networks and Personal networks
Reading:
- Watts Six Degrees. preface-Ch.1
- Easley and Kleinberg Networks, Crowds, and Markets. Ch.1-Ch.2
- [optional] Fischer, Still connected, esp. Ch. 2 and 7
Network structure: foundations
Reading:
- Easley and Kleinberg Networks, Crowds, and Markets. Ch. 3.1-3.3 (Triadic closure + tie strength)
- Easley and Kleinberg Networks, Crowds, and Markets. Ch. 3.5 (Social capital)
- Easley and Kleinberg Networks, Crowds, and Markets. Ch.5.1-5.2 (Positive and negative relationships)
- Friends you can count on
- Why your friends have more friends than you do
- Watts Six Degrees. Ch. 2 (Random networks)
- TBD - possibly some empirical examples
Small worlds and beyond
Readings:
- Easley and Kleinberg Networks, Crowds, and Markets. Ch.4.3-4.4 (Affiliation networks)
- Watts Six Degrees. Ch. 3 (Small worlds)
- Watts Six Degrees. Ch. 4 (Beyond small worlds; scale-free networks)
- Easley and Kleinberg Networks, Crowds, and Markets. Ch. 20.1-20.2 (Small worlds)
- Watts Six Degrees. Ch. 5 (Search in networks)
- Easley and Kleinberg Networks, Crowds, and Markets. Ch. 20.3-20.5 (Search in small worlds)
- Easley and Kleinberg Networks, Crowds, and Markets. Ch. 18.1-18.5 (Scale-free networks)
Network structure: advanced
Reading:
TBD
Simple contagion
Reading:
- Watts Six Degrees. Ch.6
- Easley and Kleinberg Networks, Crowds, and Markets. Ch. 21.1-21.3 (The SIR epidemic model)
Concurrency in sexual networks
- Sexual networks, concurrency, and HIV
- Epstein The Invisible Cure. Ch.2-4
- OPTIONAL: Easley and Kleinberg Networks, Crowds, and Markets. Ch. 21.6
- NOTE: if you are interested in reading more of the debate over concurrency, this issue of the journal that Lurie and Rosenthal published in has papers on both sides. (These additional papers are not required reading.)
Complex contagion
Reading:
- Watts Six Degrees. Ch. 8
- Easley and Kleinberg Networks, Crowds, and Markets. Ch. 19.1-19.6
- Study says obesity can be contagious
Other class policies
Religious Accommodations
Requests to accommodate a student’s religious creed by scheduling tests or examinations at alternative times should be submitted directly to the instructor. Reasonable common sense, judgment and the pursuit of mutual goodwill should result in the positive resolution of scheduling conflicts. The regular campus appeals process applies if a mutually satisfactory arrangement cannot be achieved.
Statement on Academic Freedom
Both students and instructors have rights to academic freedom. Please respect the rights of others to express their points of view in the classroom.
DSP Accommodations
The purpose of academic accommodations is to ensure that all students have a fair chance at academic success. Disability, or hardships such as basic needs insecurity, uncertain documentation and immigration status, medical and mental health concerns, pregnancy and parenting, significant familial distress, and experiencing sexual violence or harassment, can affect a student’s ability to satisfy particular course requirements. Students have the right to reasonable academic accommodations, without having to disclose personal information to instructors. For more information about accommodations, scheduling conflicts related to religious creed or extracurricular activities, please see the Academic Accommodations hub website.
Student Resources
The Student Learning Center provides a wide range of resources to promote learning and academic success for students. For information regarding these services, please consult the Student Learning Center Website: https://slc.berkeley.edu/
Classroom Climate
We are all responsible for creating a learning environment that is welcoming, inclusive, equitable, and respectful. If you feel that these expectations are not being met, you can consult your instructor(s) or seek assistance from campus resources (see the Academic Accommodations website).
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:
- Any work submitted should be your own individual thoughts, and should not have been submitted for credit in another course unless you have prior written permission to re-use it in this course from this instructor.
- All assignments must use “proper attribution,” meaning that you have identified the original source and extent or words or ideas that you reproduce or use in your assignment. This includes drafts and homework assignments!
- In general, you should not turn in work that was done by an AI tool, such as an LLM (like ChatGPT). If you have any questions, please ask an instructor.
- If you are unclear about expectations, ask your instructor or GSI.
- Do not collaborate or work with other students on assignments or projects unless you have been given permission or instruction to do so.