Social Networks (Demography 180)

(Syllabus last updated: 2024-November-11)

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

Theme Week Date Topic Demo Lab Hwk

Intro + Personal Networks

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

Hwk 1: Collecting personal network survey data

2

 Tue, Sep 3 Personal networks; social connectedness and social isolation in America

Lab 1: Analyzing personal network data; review of bootstrap

Network structure: foundations

2

 Thu, Sep 5 Triadic closure

Clustering coefficient; Triadic closure in an email network

Hwk 2: Analyzing personal network data

3

 Tue, Sep 10 Strength of Weak Ties, Social Capital, Structural Holes

Strength of weak ties demo

Lab 2: Getting started with complete network data

3

 Thu, Sep 12 Network centrality / the Friendship Paradox

Hwk 3: Complete network data

4

 Tue, Sep 17 Networks in context; homophily; affiliation networks; and foci

Two mode network demo

Lab 3: Enriching network data and testing a hypothesis about homophily

4

 Thu, Sep 19 Positive and negative networks

Structural balance demo


5

 Tue, Sep 24 Intro to mathematical network models; the Erdos-Renyi model and its predictions

ER random networks demo

Lab 4 - Exploring the ER model

Small worlds

5

 Thu, Sep 26 Small worlds

Hwk 4: Problem set I

6

 Tue, Oct 1 Search in small worlds

Lab 5 - Two-mode networks

6

 Thu, Oct 3 Scale-free networks

netlogo BA model

Hwk 5: Advanced complete network data

7

 Tue, Oct 8 Midterm review



7

 Thu, Oct 10 Midterm


Network structure: advanced

8

 Tue, Oct 15 More models: configuration model and stochastic block model

configuration model; SBM


8

 Thu, Oct 17 Community detection

community detection


9

 Tue, Oct 22 Empirical studies of network structure

Lab 6: Community detection

Dynamics: Simple contagion

9

 Thu, Oct 24 Diseases and simple contagion in general; SIR model

SIR demo

Hwk 6: Problem set II

10

 Tue, Oct 29 SIR model on networks; centrality, influence and network disease models

Network SIR demo; Threshold infectiousness demo

Lab 7: Simple contagion

Concurrency

10

 Thu, Oct 31 Sexual networks, concurrency, and HIV

Concurrency demo

Hwk 7: Centrality and the SIR model

11

 Tue, Nov 5 Empirical studies of simple contagion

Lab 8: Mini-project prep

Social influence

11

 Thu, Nov 7 Social influence, herding, and cascades

Mini-project

12

 Tue, Nov 12 Threshold models and complex contagion


Dynamics: Complex contagion and social influence

12

 Thu, Nov 14 Complex contagion on networks



13

 Tue, Nov 19 TBD - likely guest lecture



13

 Thu, Nov 21 Complex contagion on networks, cont.



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


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:

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.

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:

Network structure: foundations

Reading:

Small worlds and beyond

Readings:

Network structure: advanced

Reading:

TBD

Simple contagion

Reading:

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.)

Social influence

Reading:

Complex contagion

Reading:

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.

References

Easley, David, 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/.
Epstein, Helen. The Invisible Cure: Why We Are Losing the Fight Against AIDS in Africa. Macmillan, 2008.
Watts, D. J. Six Degrees: The Science of a Connected Age. WW Norton & Company, 2003.