Class meetings: Tuesdays and Thursdays, 8:00-9:30AM
Office hours: (See Piazza post)
Email: feehan [at]

Piazza page:
Gradescope page:
Lecture slides:
Bcourses page:
Final exam: Wed, Dec 15, 3:00P - 6:00P (Exam Location TBD)

(Syllabus last updated: 2021-November-17)


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 26 Intro / what social networks are / class info
2 Tue, Aug 31 Basic graph theory: definitions, types of networks, types of network data; survey data collection Lab 0 Hwk 1
Personal networks 2 Thu, Sep 2 Personal networks; social connectedness and social isolation in America personal networks demo Lab 1_1 Lab 1_2
3 Tue, Sep 7 Working with personal network data; our survey results
Complete network data 3 Thu, Sep 9 Working with entire network data; quantifying network structure whole network demo Hwk 2
Network models: the ER model 4 Tue, Sep 14 Intro to mathematical network models; the Erdos-Renyi model and its predictions ER random networks demo Lab 2
Homophily / Tie strength 4 Thu, Sep 16 Strength of weak ties; social capital Triadic closure in an email network
5 Tue, Sep 21 Networks in context; homophily Strength of weak ties demo Lab 3 Hwk 3
Balance theory 5 Thu, Sep 23 Positive and negative relationships Structural balance demo
Affiliation networks and foci 6 Tue, Sep 28 Affiliation networks; foci; group membership; one-mode projections of bipartite networks Lab 4 Hwk 4
Small worlds 6 Thu, Sep 30 Small worlds
7 Tue, Oct 5 Search in small worlds Hwk 5
Scale-free networks 7 Thu, Oct 7 Scale-free networks BA model
8 Tue, Oct 12 Midterm review
8 Thu, Oct 14 Midterm
Simple contagion 9 Tue, Oct 19 Midterm review; Diseases and simple contagion in general; SIR model SIR demo
10 Tue, Oct 26 SIR model on networks network SIR demo Lab 5
10 Thu, Oct 28 Centrality, influence, and network disease models threshold infectiousness demo
Concurrency 11 Tue, Nov 2 Sexual networks, concurrency, and HIV concurrency demo Lab 6
11 Thu, Nov 4 Guest speaker: Casey Breen, Quantifying interpersonal contact patterns; network-based sampling
12 Tue, Nov 9 Guest speaker: Ethan Roubenoff, Spatial Demography Hwk 6
Social influence 13 Tue, Nov 16 Social influence, herding, and cascades Hwk 7
Complex contagion 13 Thu, Nov 18 Threshold models and complex contagion
14 Tue, Nov 23 Complex contagion on networks
15 Tue, Nov 30 Complex contagion on networks, cont.
Empirical studies of contagion 15 Thu, Dec 2 Is obesity contagious? Experimental and observational studies of complex contagion / Wrap up
16 Tue, Dec 7 READING WEEK
16 Thu, Dec 9 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.

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

  • 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 5 to 7 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

Detailed modules

Introduction to social networks

Intro to social networks; course overview


  • Intro / what social networks are / class info
  • Basic graph theory: definitions, types of networks

Other resources:


  • Watts Six Degrees. preface-Ch.1
  • Easley and Kleinberg Networks, Crowds, and Markets. Ch.1-Ch.2

Personal networks


  • 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

  • Lecture demo: Personal networks of Berkeley students

Other resources:


  • Easley and Kleinberg Networks, Crowds, and Markets. Ch.2 (Graphs)

Working with complete network data


  • Working with entire network data; quantifying network structure: degree distributions, average path length, clustering
  • Lecture demo

Other resources:

  • Lab 2
  • Hwk 3

Network models: the ER model



  • Watts Six Degrees. Ch. 2

Tie strength and homophily


  • Strength of weak ties; social capital
  • Networks in context; homophily: choice and structure; social implications
  • Using a null model to analyze empirical data; homophily example
  • Lecture demo: Strength of weak ties in the wiki talk network

Other resources:


  • Easley and Kleinberg Networks, Crowds, and Markets. Ch. 3.1-3.3 (Tie strength)
  • Easley and Kleinberg Networks, Crowds, and Markets. Ch. 3.5 (Social capital)
  • Easley and Kleinberg Networks, Crowds, and Markets. Ch.4.1-4.2 (Homophily)

Balance theory



  • Easley and Kleinberg Networks, Crowds, and Markets. Ch.5.1-5.2 (Positive and negative relationships)

Affiliation networks


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


  • Easley and Kleinberg Networks, Crowds, and Markets. Ch.4.3-4.4 (Affiliation networks)

Small worlds


  • The small world phenomenon


  • Watts Six Degrees. Ch. 3-4
  • Easley and Kleinberg Networks, Crowds, and Markets. Ch. 20.1-20.2 (Small worlds)

Search in small worlds and scale-free networks



  • Watts Six Degrees. Ch. 4-5
  • 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)

Catch-up, review, and midterm


  • Catch-up and midterm review

Simple contagion


  • 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


  • Watts Six Degrees. Ch.6
  • Notebook: Centrality and the SIR model on ER random networks (to be posted)

Concurrency in sexual networks

  • Sexual networks, concurrency, and HIV
  • Epstein The Invisible Cure. Ch.2-4
  • Mark N. Lurie and Samantha Rosenthal “Concurrent Partnerships as a Driver of the HIV Epidemic in Sub-Saharan Africa? The Evidence Is Limited,” AIDS and Behavior 14, no. 1 (2010): 17–24.
  • 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


  • Social influence
  • Threshold models


  • Easley and Kleinberg Networks, Crowds, and Markets. Ch. 16.1-16.2; parts of 16.3-16.6; 16.7
  • Watts Six Degrees. Ch. 7

Complex contagion


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


  • Watts Six Degrees. Ch. 8
  • Easley and Kleinberg Networks, Crowds, and Markets. Ch. 19.1-19.6

Empirical studies of contagion


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.

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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.