Introduction to Social Networks (SOC 321K)
University of Texas at Austin, Department of Sociology
Meeting Times: Tue/Thu 09:30 AM – 11:00 AM
Meeting Location: RLP 0.102
Course Instructor:
Casey Breen (Email:
casey.breen@austin.utexas.edu)
🕒 Office Hours Sign-up (Tuesdays from 11:30am–2:00pm):
Book Link
Course Overview
This course introduces the science of social networks—how people are connected and how those connections shape social behavior, opportunities, and outcomes. We will use concepts and methods from the social, natural, and mathematical sciences to define networks, analyze network data, and examine how networks are applied in both academic research and practice. The course draws on examples from public health (e.g., HIV prevention at CDC and UNAIDS), sociology (e.g., how friendship networks influence educational outcomes), and technology (e.g., the rise and diffusion of social media platforms in Silicon Valley). We will combine theory, empirical research, and hands-on data analysis to build a foundational understanding of social networks.
Learning Outcomes
At the conclusion of this course, students will be able to:
- Understand the components and properties of social networks
- Critically engage with classic and contemporary social network research
- Evaluate how social networks structure the social world and shape life outcomes
Assignments and weight
- Exam 1 — 20%
- Exam 2 — 20%
- Homeworks — 15%
- Labs — 15%
- Final Podcast Project — 20%
- Attendance — 10%
Class Schedule
Daily Schedule
January 13 — Lecture 1
Welcome! Introduction to course + syllabus; examples of social network research
- Course syllabus
January 15 — Lecture 2
Basic graph theory / giant component / degree distributions
- Easley and Kleinberg Ch 1, Ch 2
- Borgatti, Stephen P., Ajay Mehra, Daniel J. Brass, and Giuseppe Labianca. 2009. “Network Analysis in the Social Sciences.” Science.
January 20 — Lecture 3
Personal networks; social connectedness and social isolation in America
- McPherson, Miller, Lynn Smith-Lovin, and Matthew E. Brashears. 2006. “Social Isolation in America: Changes in Core Discussion Networks over Two Decades.” American Sociological Review.
Note: Please install R and RStudio in advance of next sesion, which is an R lab.
January 22 — Lab 1
Intro to R and iGraph package
- Textbook: R for Data Science [For reference; not required reading]
Lab 1 Due
January 27 — Lecture 4
Personal networks (cont); triadic closure
- Easley and Kleinberg, Ch. 3.1–3.3
January 29 — Lecture 5
Structural Balance
- Easley and Kleinberg, Ch. 5.1–5.2
February 3 — Lecture 6
Strength of weak ties / structural holes / social capital
- Easley and Kleinberg, Ch. 3.2
- Granovetter, Mark S. 1973. “The Strength of Weak Ties.” American Journal of Sociology.
February 5 — Lecture 7
Network Models and Small Worlds
-
Easley and Kleinberg, Ch. 20.1-20.2
-
Watts Ch. 2 (Random networks), Ch. 3 (Small worlds)
February 10 — Lecture 8
The friendship paradox and node centrality
- Feld, Scott L. 1991. “Why Your Friends Have More Friends Than You Do.” American Journal of Sociology.
February 12 — Exam 1 Review
Exam Review
February 17 — Exam 1
Exam 1
February 19 — Lecture 9
Homophily
- Easley and Kleinberg, Ch. 4.1 and 4.2
- Currarini, Sergio, Matthew O. Jackson, and Paolo Pin. 2010. “Identifying the Roles of Race-Based Choice and Chance in High School Friendship Network Formation.” Proceedings of the National Academy of Sciences.
February 24 — Lecture 10
Contagions Part I
- Easley and Kleinberg, Ch. 21.1–21.3
- Watts Ch. 6
February 26 — Lecture 11
Contagions Part II
- Easley and Kleinberg, Ch. 19.1–19.6
March 3 — Lecture 12
Social influence, herding, and cascades
- Watts Ch. 8
March 5 — Lab 2
Visualizing network data and calculating homophily indices
Lab 2 Due
March 10 — Lecture 13
Community Detection
March 12 — Lecture 14
Empirical Studies of Contagion
- Centola, Damon. 2010. “The Spread of Behavior in an Online Social Network Experiment.” Science.
Homework 2 Due
March 17 — Spring Break
No Class
March 19 — Spring Break
No Class
March 24 — Lecture 15
Guest Lecture: RDS in Practice
- Salganik, Matthew J., and Douglas D. Heckathorn. 2004. “Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling.” Sociological Methodology.
- Breen, Casey F., et al. 2025. “Estimating Death Rates in Complex Humanitarian Emergencies Using the Network Survival Method.” American Journal of Epidemiology.
- Feehan, Dennis M., and Matthew J. Salganik. 2016. “Generalizing the Network Scale-up Method.” Sociological Methodology.
March 26 — Concurrency and the HIV Epidemic
March 31 — Exam 2 Review
Exam 2 Review
April 2 — Exam 2
Exam 2
April 7 — Lecture 17
Final Project Overview + Class Project Meet-up Day
April 9 — Lecture 18
Class Wrap-up
- Rand, David G., Samuel Arbesman, and Nicholas A. Christakis. 2011. “Dynamic Social Networks Promote Cooperation in Experiments with Humans.” PNAS.
Homework 3 Due
April 14 — Lab 3
Network scale-up method
- Breen, Casey F., et al. 2025. “Estimating Death Rates in Complex Humanitarian Emergencies Using the Network Survival Method.” American Journal of Epidemiology.
- Feehan, Dennis M., and Matthew J. Salganik. 2016. “Generalizing the Network Scale-up Method.” Sociological Methodology.
Lab 3 Due
April 16 — Presentation Prep
Final Project Prep (Flex Day)
April 21 — Presentations
Mini–conference — Final Presentation
April 23 — Presentations
Mini–conference — Final Presentation
Class Attendance
In this course, attendance at these meetings is mandatory and part of your grade.
Every student is allowed four (4) free absences with no questions asked. In other words, you do
not have to inform me (or your TA) that you are missing class. For every absence after the
allotted four (4), you lose 2.5% of the possible 10% for attendance/participation. You will receive a
0% for class attendance/participation after eight (8) absences.
Please do not email us if you are using a free absence from lecture “just to let us know.” You
can just not show up!
Religious Holy Days
By UT Austin policy, you must notify me of your pending absence for a religious holy day as far in
advance as possible of the date of observance. If you must miss a class, an examination, a work
assignment, or a project in order to observe a religious holy day, you will be given an opportunity to
complete the missed work within a reasonable time after the absence. For questions regarding
religious accommodations, please contact the Office of the Dean of Students
Academic Integrity
Academic integrity is foundational to scholarly work. To learn more about academic integrity
standards, tips for avoiding a potential academic misconduct violation and the overall conduct
process, please visit the Student Conduct and Academic Integrity website. I have a 0-tolerance policy
towards any type of academic misconduct.
Letter Grade Percentage
Final grades will be based on the standard UT grading scale. Grades will use +/-. Grades will not
be curved.
A 93% & above; A- 90% – 92.9%; B+ 87% – 89.9%; B 83% – 86.9%; B- 80% – 82.9%;
C+ 77% – 79.9%; C 73% – 76.9%; C- 70% – 72.9%; D+ 67% – 69.9%; D 63% – 66.9%;
D- 60% – 62.9%; F 59.9% and below
Incompletes
Incompletes (I) are given only when a student is unable to complete a segment of the course
because of circumstances beyond the student’s control. To be considered for an incomplete a
student must have completed two-thirds of all course work with at least a satisfactory grade. An
‘incomplete’ is never granted automatically and each request is carefully reviewed by both the
Professor and the Teaching Assistant.
Acknowledgements
This course is largely based on a course originally developed by the wonderful Prof. Dennis Feehan at UC Berkeley. I am grateful for his generosity in sharing materials and pedagogical approaches that informed the structure and content of this class.