MS&E 135: Networks

Spr 2025-26

Tu, Th 9:00-10:20am
Shriram 104

INSTRUCTOR
Ashish Goel
ashishg@stanford.edu, 650 814 1478
Office Hours: Mon 5-6 pm in person (HEC 308)

TEACHING ASSISTANTS
Naman Gupta
namang@stanford.edu
Erica Zhou
ericaez@stanford.edu
Megan Ja
meganja@stanford.edu

TA Office Hours: Mon 8-9 pm on Zoom, Wed 5-6 pm in person (Huang B007)

IMPORTANT LINKS
Please see OH Zoom links, Attendance Google Form, Ed sign-up, and Gradescope entry code here.

IMPORTANT NOTES

We will not have Office hours in the first week of class.
Unless an email is confidential for the instructor, or has very specific request for one person, please send it to msande135-spr2526-staff@lists.stanford.edu so we can track internally. Emails sent to only one person sometimes get lost.
Class forum: We will use Ed Stem as a discussion forum, and Gradescope for assignments. We will use Canvas for posting handouts.
Auditors: please sign on to the mailman list msande135-spr2526-guests and let the class staff know.
Attendance is mandatory for at least 80% of the lectures. There will be an attendance tracking mechanism (the first week is exempt).
Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE).

DESCRIPTION and PRE-REQS
This course provides an introduction to how networks underlie our social, technological, and natural worlds, with an emphasis on developing intuitions for broadly applicable concepts in network analysis. The course will include: an introduction to graph theory and graph concepts; social networks; information networks; the aggregate behavior of markets and crowds; network dynamics; information diffusion; the implications of popular concepts such as “six degrees of separation”, the “friendship paradox”, and the “wisdom of crowds”. No advanced mathematical knowledge is assumed. We will use some basic probability (random variables, expectation, independence), and will briefly review these when they are first introduced.

PHILOSOPHY AND LEARNING GOALS
The class aims to provide an Engineering perspective on networked systems, specially those that are socio-economic in nature (e.g. we will not discuss how the Internet routing protocols work). We will take “A first course” approach and focus on intuition. There will be no coding (except for demo purposes or generated by AI agents).

The learning goals are:

Understand basic graph theory and network analysis
Understand basic game theory and strategic behavior
Develop a quantitative and qualitative intuition for the role of networks in social and technological systems.

DETAILED PLAN
All chapters below refer to the text by Easley and Kleinberg: Networks, Crowds, and Markets, Cambridge University Press, 2010. The text is available online for free and also available as a reasonably priced hard-cover. This is subject to change as the class is being continually updated. You can also access slides from last year if you want to look ahead (but please note that they may change somewhat this year).

Week	Day	Topic	Reading	Assignments
Week 1	Tu	Course overview; Introduction to graph theory	Ch 1, 2.1-2.3	Visit Canvas
Week 1	Th	Strong and weak ties	Ch 3.1-3.3	PS0 and PS1 handed Out
Week 2	Tu	Homophily, Affiliation; Friendship paradox	Ch 4.1-4.3
Week 2	Th	Structural balance	Ch 5.1-5.4	PS0 due the next day at 5pm
Week 3	Tu	Game theory	Ch 6.1-6.9	PS1 due at 5pm; PS2 handed out
Week 3	Th	Congestion, Auctions	Ch 8.1-8.2, 9.1-9.2	Collaborative project details published
Week 4	Tu	Matching markets	Ch 9.3-9.6, 10.1-10.4
Week 4	Th	Bargaining & power	Ch 12.1-12.3, 12.5-12.8	PS2 due the next day at 5pm; PS3 Out
Week 5	Tu	The web as a network	Ch 13.1-13.5
Week 5	Th	In-class Midterm
Week 6	Tu	Link Analysis	Ch 14.1-14.3	Collaborative project teams and preliminary statement due
Week 6	Th	Web search	Ch 14.4-14.5
Week 7	Tu	Sponsored search as a market	Ch 15.1-15.5
Week 7	Th	Information cascades	Ch 16.1-16.7	PS3 due the next day at 5pm; PS4 Out
Week 8	Tu	Network effects, cascading behavior	Ch 17.1-17.3, 19.1-19.4
Week 8	Th	Rich-get-richer	Ch 18.1-18.6
Week 9	Tu	Small worlds	Ch 20.1-20.6
Week 9	Th	Epidemics	Ch 21.1-21.4, 21.6	PS4 due the next day at 5pm; Collaborative project due; PS5 Out
Week 10	Tu	Finish any material that did not get covered; special topics
Week 10	Th	Presentations from selected collaborative projects		PS5 due the next day at 5pm
Final	Mon	June 8, 8:30am-11:30am

Slides will be posted on canvas or emailed by the start of lectures.

REQUIREMENTS

There will be 5 regular Problem Sets (25% of class grade), and one extra ungraded Problem Set (PS0) that will be a check-list confirming that you have done the basic things for the class: downloaded the text, read the syllabus, read the collaboration rules, etc.
There will be one midterm (20%) and one final exam (30%). The final is cumulative, but with a higher emphasis on post-midterm content. Both exams are open-book but closed electronics.
There will be one collaborative project (details TBD, due after midterm, 15%)
Attendance is mandatory for at least 80% of the lectures (10%). The first week of classes is exempt.
Problem Set Rules:
1. All problem sets are to be submitted by 5pm on the due date. Submissions will be made online, through Gradescope.
2. Collaboration policy: You may discuss the problem sets with other students in the class, but since the goal is to practice skills, the actual writing up of the solutions must be done separately. You can use AI agents to polish your writing, and for suggestions on how to solve the problem, but the first draft of the solution must be written by you. You are responsible for the content that AI generated (you lose points for mistakes made by an AI tool). You need to understand the AI generated content, and make sure your final answer is to the point and concise.
3. Late Policy: No late assignments will be accepted.
4. Grading: The lowest problem set grade will be dropped, and the remaining problem sets will receive equal weight. Effectively the last two bullets mean that if you are unable to submit one problem set by the deadline, you will be “dropping” that problem set and graded on the remaining 4 problem sets. It’s not advisable to miss more than one deadline.
5. Exceptional circumstances: For medical/family emergencies, feel free to write to the course staff and we will try to accommodate if feasible. If you have to miss more than 20% of the lectures due to athletic obligations, please have your academic advisor or coach write to us.