Social Networks (Demography 180)

(Syllabus last updated: 2023-November-17)

Staff

Professor Dennis Feehan, feehan [at] berkeley.edu
Office hours: (see Ed post)

GSI Christina Misunas, cmisunas [at] berkeley.edu
Office hours: (see Ed post)

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 Lecture Lab Hwk

Intro

1

 Thu, Aug 24 Intro / what social networks are / class info

2

 Tue, Aug 29 Basic graph theory, working with complete networks

Intro to whole network data

Lab 0

Personal networks

2

 Thu, Aug 31 Personal networks; social connectedness and social isolation in America

Hwk 1

Triadic closure / SOWT

3

 Tue, Sep 5 Triadic closure, strength of weak ties

Triadic closure in an email network

Lab 1

Tie strength / social capital / homophily

3

 Thu, Sep 7 Social capital and structural holes

Strength of weak ties demo

Hwk 2

Homophily and balance theory

4

 Tue, Sep 12 Networks in context; homophily; positive and negative networks

Structural balance demo

Lab 2

Network models: the ER model

4

 Thu, Sep 14 Intro to mathematical network models; the Erdos-Renyi model and its predictions

ER random networks demo

Hwk 3

Small worlds

5

 Tue, Sep 19 Small worlds

Lab 3

5

 Thu, Sep 21 Search in small worlds

Hwk 4

Affiliation networks and foci

6

 Tue, Sep 26 Affiliation networks; foci; group membership; one-mode projections of bipartite networks

Two mode network demo

Lab 4

Scale-free networks

6

 Thu, Sep 28 Scale-free networks

netlogo ba model

Hwk 5

7

 Tue, Oct 3 Configuration model AND/OR community detection

Community detection

Empirical studies of network structure

7

 Thu, Oct 5 Empirical studies of network structure

8

 Tue, Oct 10 Midterm review

Scale-free networks

8

 Thu, Oct 12 Midterm

Simple contagion

9

 Tue, Oct 17 Diseases and simple contagion in general; SIR model

sir thresh demo

Lab 5

9

 Thu, Oct 19 SIR model on networks

SIR demo

10

 Tue, Oct 24 Centrality, influence, and network disease models / Empirical studies of simple contagion

network SIR demo

Hwk 6

Concurrency

10

 Thu, Oct 26 Sexual networks, concurrency, and HIV

Social influence

11

 Tue, Oct 31 Social influence, herding, and cascades

11

 Thu, Nov 2 NO CLASS

Complex contagion

12

 Tue, Nov 7 Threshold models and complex contagion

12

 Thu, Nov 9 Complex contagion on networks

13

 Tue, Nov 14 Complex contagion on networks, cont.

Empirical studies of contagion

13

 Thu, Nov 16 Is obesity contagious? Experimental and observational studies of complex contagion

Hwk 7

14

 Tue, Nov 21 NO CLASS

14

 Thu, Nov 23 THANKSGIVING (NO CLASS)

15

 Tue, Nov 28 Friendship Paradox

15

 Thu, Nov 30 Wrap up

16

 Tue, Dec 5 READING WEEK

16

 Thu, Dec 7 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 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.

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.

Quizzes

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).

Summary

Component % of grade
Homeworks (you can drop your lowest score) 30
Labs 20
Mid-term exam 20
Final exam 25
Quizzes 5

Detailed modules

Introduction to social networks

Intro to social networks; course overview

Lectures:

  • Intro / what social networks are / class info
  • Basic graph theory: definitions, types of networks; working with complete network data; degree distributions, average path length, clustering

Other resources:

  • Lab 0: some Python basics
  • Lab 1: some Python basics
  • Hwk 2: the clustering coefficient

Reading:

Personal networks

Lectures:

  • 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

Other resources:

  • Hwk 1: Collecting personal network data

Triadic closure / strength of weak ties / social capital

Lectures:

  • Triadic close; strength of weak ties
  • Social capital; structural holes

Other resources:

Reading:

Homophily and balance theory

Lectures:

  • Networks in context; homophily: choice and structure; social implications
  • Positive and negative relationships

Other resources: * Lab - Using a null model to analyze empirical data; homophily example

Reading:

Network models: the ER model

Lectures:

  • Intro to mathematical network models; the Erdos-Renyi model and its predictions

Readings:

Affiliation networks

Lectures:

  • Affiliation networks; foci; group membership; one-mode projections of bipartite networks

Readings:

Small worlds

Lectures:

  • The small world phenomenon

Readings:

Search in small worlds and scale-free networks

Lectures:

  • Search in small world
  • Scale-free networks

Readings:

Empirical studies of network structure

We will discuss results from several recent research papers relevant to network structure.

Catch-up, review, and midterm

Lectures:

  • Catch-up and midterm review

Simple contagion

Lectures:

  • SIR model, types of centrality
  • Easley and Kleinberg Networks, Crowds, and Markets. Ch. 21.1-21.3 (The SIR epidemic model)
  • Why your friends have more friends than you do; network structure and disease; measuring contact networks

Reading:

  • Watts Six Degrees. Ch.6
  • Notebook: Centrality and the SIR model on ER random networks

Homework 4

  • Homework 4 - SIR models and centrality (to be posted)

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

Lectures:

  • Social influence
  • Threshold models

Reading:

Complex contagion

Lectures:

  • Complex contagion; decisions, threshold models
  • Experimental studies of complex contagion
  • Is obesity contagious? Observational studies of network contagion

Reading:

The friendship paradox

Homework 6 and Quiz 3

Reading:

Empirical studies of 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!
  • 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.