• Lectures: are on Tuesday/Thursday 3:00-4:20pm in person in the NVIDIA Auditorium.
  • Lecture Videos: are available on Canvas for all the enrolled Stanford students.
  • Public resources: The lecture slides and assignments will be posted online as the course progresses. We are happy for anyone to use these resources, but we cannot grade the work of any students who are not officially enrolled in the class.
  • Contact: Students should ask all course-related questions on Ed (accessible from Canvas), where you will also find announcements. For external inquiries, personal matters, or in emergencies, you can email us at cs224w-win2223-staff@lists.stanford.edu.
  • Academic accommodations: If you need an academic accommodation based on a disability, you should initiate the request with the Office of Accessible Education (OAE). The OAE will evaluate the request, recommend accommodations, and prepare a letter for the teaching staff. Once you receive the letter, send it to our staff email address. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations.


What is this course about?

Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks.
Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis.

Previous Offerings

You can access slides and project reports of previous versions of the course on our archived websites: CS224W: Fall 2021 / CS224W: Winter 2021 / CS224W: Fall 2019 / CS224W: Fall 2018 / CS224W: Fall 2017 / CS224W: Fall 2016 / CS224W: Fall 2015 / CS224W: Fall 2014 / CS224W: Fall 2013 / CS224W: Fall 2012 / CS224W: Fall 2011 / CS224W: Fall 2010


Students are expected to have the following background:

  • Knowledge of basic computer science principles, sufficient to write a reasonably non-trivial computer program (e.g., CS107 or CS145 or equivalent are recommended)
  • Familiarity with the basic probability theory (CS109 or Stat116 are sufficient but not necessary)
  • Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary)

The recitation sessions in the first weeks of the class will give an overview of the expected background.

Course Materials

Notes and reading assignments will be posted periodically on the course Web site. The following books are recommended as optional reading:


Lecture slides will be posted here shortly before each lecture.

This schedule is subject to change. All assignment deadlines are at 11:59pm PT.

Date Description Optional Readings Events Deadlines
Tue 1/10 1. Introduction
Thu 1/12 2. Feature Engineering for ML in Graphs
Colab 0, Colab 1 out
Tue 1/17 3. Node Embeddings
Thu 1/19 4. Graph Neural Networks
Homework 1 out
Tue 1/24 5. A General Perspective on GNNs
Thu 1/26 6. GNN Augmentation and Training
Colab 2 out Colab 1 due
Tue 1/31 7. Theory of Graph Neural Networks
Thu 2/2 8. Label Propagation on Graphs
Homework 2 out LaTeX template Homework 1 due
Tue 2/7 9. Machine Learning with Heterogeneous Graphs
Project Proposal
Thu 2/9 10. Knowledge Graph Embeddings
Colab 3 out Colab 2 due
Tue 2/14 11. Reasoning over Knowledge Graphs
Thu 2/16 12. Fast Neural Subgraph Matching and Counting
Homework 3 out LaTeX template
Sat 2/18 No class Homework 2 due
Tue 2/21 13. GNNs for Recommender Systems
Thu 2/23 14. Deep Generative Models for Graphs
Colab 4 out Colab 3 due
Tue 2/28 15. Advanced Topics in GNNs
Thu 3/2 16. Scaling Up GNNs to Large Graphs
Guest Lecture: Weihua Hu
Colab 5 out Homework 3 due
Tue 3/7 Exam
Thu 3/9 17. Geometric Graph Learning
Guest Lecture: Minkai Xu
Colab 4 due
Tue 3/14 18. Trustworthy Graph AI
Guest Lecture: Rex Ying
Colab 5 due
Thu 3/16 19. Conclusion
Tue 3/21 No class Project report due