This class will be offered next in Fall 2021.
The first meeting of the class will be on Tuesday September 21 2021.

Logistics

Instructor


Content

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: 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

Prerequisites

Students are expected to have the following background:

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:


Schedule

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 Suggested Readings / Important Notes Events Deadlines
Tue Sep 21 1. Introduction; Machine Learning for Graphs
[slides]
Thu Sep 23 2. Traditional Methods for ML on Graphs
[slides]
Colab 0, Colab 1 out
Tue Sep 28 3. Node Embeddings
[slides]
Thu Sep 30 4. Link Analysis: PageRank
[slides]
Homework 1 out
Tue Oct 5 5. Label Propagation for Node Classification
[slides]
Thu Oct 7 6. Graph Neural Networks 1: GNN Model
[slides]
Colab 2 out Colab 1 due
Tue Oct 12 7. Graph Neural Networks 2: Design Space
[slides]
Thu Oct 14 8. Applications of Graph Neural Networks
[slides]
Homework 2 out LaTeX template Homework 1 due
Tue Oct 19 9. Theory of Graph Neural Networks
[slides]
Project Proposal
due
Thu Oct 21 10. Knowledge Graph Embeddings
[slides]
Colab 3 out Colab 2 due
Tue Oct 26 11. Reasoning over Knowledge Graphs
[slides]
Thu Oct 28 12. Frequent Subgraph Mining with GNNs
[slides]
Homework 3 out LaTeX template Homework 2 due
Tue Nov 2 NO CLASS - DEMOCRACY DAY
Thu Nov 4 13. GNNs for Recommender Systems
[slides]
Colab 4 out Colab 3 due
Tue Nov 9 14. Community Structure in Networks
[slides]
Thu Nov 11 15. Deep Generative Models for Graphs
[slides]
Colab 5 out Homework 3 due
Tue Nov 16 16. Advanced Topics on GNNs
[slides]
Thu Nov 18 17. Scaling Up GNNs
[slides]
Colab 4 due
Fri Nov 19 Exam [Review slides]
Tue Nov 23 NO CLASS - THANKSGIVING BREAK
Tue Nov 25 NO CLASS - THANKSGIVING BREAK
Tue Nov 30 18. Guest Lecture: Petar Veličković
Note: No in-person class this day, please attend lecture on Zoom (see Ed for Zoom link).
Thu Dec 2 19. GNNs for Science
Colab 5 due
Thu Dec 9 Project Report due