Logistics


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: 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 deadlines are at 11:59pm PT except for project proposal and report (which will be at 12:00pm PT).

Date Description Suggested Readings / Important Notes Events Deadlines
Tue Jan 12 1. Introduction; Machine Learning for Graphs
[slides]
Thu Jan 14 2. Traditional Methods for ML on Graphs
[slides]
Colab 0, Colab 1 out
Tue Jan 19 3. Node Embeddings
[slides]
Thu Jan 21 4. Link Analysis: PageRank
[slides]
Homework 1 out
Tue Jan 26 5. Label Propagation for Node Classification
[slides]
Thu Jan 28 6. Graph Neural Networks 1: GNN Model
[slides]
Colab 2 out Colab 1 due
Tue Feb 2 7. Graph Neural Networks 2: Design Space
[slides]
Thu Feb 4 8. Applications of Graph Neural Networks
[slides]
Homework 2 out Homework 1 due
Tue Feb 9 9. Theory of Graph Neural Networks
[slides]
Thu Feb 11 10. Knowledge Graph Embeddings
[slides]
Colab 3 out Colab 2 due
Tue Feb 16 11. Reasoning over Knowledge Graphs
[slides]
Project Proposal
due
Thu Feb 18 12. Frequent Subgraph Mining with GNNs
[slides]
Mon Feb 22 Homework 3 out Homework 2 due
Tue Feb 23 13. Community Structure in Networks
[slides]
Thu Feb 25 14. Traditional Generative Models for Graphs
[slides]
Colab 4 out Colab 3 due
Tue Mar 2 15. Deep Generative Models for Graphs
[slides]
Thu Mar 4 16. Advanced Topics on GNNs
[slides]
Colab 5 out
Mon Mar 8 Homework 3 due
Tue Mar 9 17. Scaling Up GNNs
[slides]
Thu Mar 11 18. Guest Lecture: GNNs for Computational Biology
[slides]
Colab 4 due
Tue Mar 16 19. Guest Lecture: Industrial Applications of GNNs
[slides]
Thu Mar 18 20. GNNs for Science
[slides]
Sun Mar 21 Project Report due