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Logistics

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-aut2425-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.

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 2023 / CS224W: Winter 2023 / 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

Prerequisites

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:

Graph Representation Learning by William L. Hamilton
Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg
Network Science by Albert-László Barabási

Schedule

Lecture slides will be posted here shortly before each lecture.

Notes are available [here].

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

Date	Description	Optional Readings	Events	Deadlines
Tue, 9/24	1. Introduction [slides]
Thu, 9/26	2. Node embeddings [slides]	DeepWalk: Online Learning of Social Representations node2vec: Scalable Feature Learning for Networks Network Embedding as Matrix Factorization	Colab 0, Colab 1 out
Tue, 10/1	3. Graph neural networks [slides]	Geometric Deep Learning: the Erlangen Programme of ML Semi-Supervised Classification with Graph Convolutional Networks
Thu, 10/3	4. A general perspective on GNNs [slides]	Design Space of Graph Neural Networks Inductive Representation Learning on Large Graphs Graph Attention Networks	Homework 1 out LaTeX template
Tue, 10/8	5. GNN augmentation and training [slides]	Hierarchical Graph Representation Learning with Differentiable Pooling
Thu, 10/10	6. Theory of GNNs [slides]	How Powerful Are Graph Neural Networks?	Colab 2 out	Colab 1 due
Tue, 10/15	7. Theory of GNNs 2 [slides]	Identity-aware Graph Neural Networks Graph Neural Networks are More Powerful than We Think Counting Graph Substructures with Graph Neural Networks Position-aware Graph Neural Networks
Thu, 10/17	8. Graph Transformers [slides]	Do Transformers Really Perform Bad for Graph Representation? Sign and Basis Invariant Networks for Spectral Graph Representation Learning Attending to Graph Transformers	Homework 2 out LaTeX template	Homework 1 due
Tue, 10/22	9. Heterogenous graphs [slides]	Modeling Relational Data with Graph Convolutional Networks Heterogeneous Graph Transformer		Project Proposal due
Thu, 10/24	10. Knowledge graphs [slides]	Translating Embeddings for Modeling Multi-relational Data Learning Entity and Relation Embeddings for Knowledge Graph Completion Embedding Entities and Relations for Learning and Inference in Knowledge Bases Complex Embeddings for Simple Link Prediction RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space	Colab 3 out	Colab 2 due
Tue, 10/29	11. Reasoning over knowledge graphs [slides]	Embedding Logical Queries on Knowledge Graphs Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings Traversing Knowledge Graphs in Vector Space
Thu, 10/31	12. GNNs for recommender systems [slides]	Neural Graph Collaborative Filtering LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation Graph Convolutional Neural Networks for Web-Scale Recommender Systems	Homework 3 out LaTeX template	Homework 2 due (Note: extended to 11/04)
Tue, 11/5	ELECTION DAY - NO CLASS
Thu, 11/7	13. Relational Deep Learning [slides]	Relational Deep Learning - Graph Representation Learning on Relational Databases RelBench: A Benchmark for Deep Learning on Relational Databases	Colab 4 out	Colab 3 due Project Milestone due
Tue, 11/12	14. Advanced topics in GNNs [slides]	PRODIGY: Enabling In-context Learning Over Graphs Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
Thu, 11/14	15. Towards Foundation Models for Knowledge Graphs [slides]	Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types Towards Foundation Models for Knowledge Graph Reasoning InGram: Inductive Knowledge Graph Embedding via Relation Graphs	Colab 5 out	Homework 3 due
Tue, 11/19	16. Deep generative models for graphs [slides]	GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Thu, 11/21	17. Geometric Deep Learning [slides]	Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation
Thu, 11/21 - Sat, 11/23	Exam [Info]
Tue, 11/26	BREAK
Thu, 11/28	BREAK
Tue, 12/3	18. LLM + GNN [slides]			Colab 4 due
Thu, 12/5	19. Conclusion [slides]	Design Space for Graph Neural Networks		Colab 5 due
Thu, 12/12				Project Report due

CS224W: Machine Learning with Graphs

Stanford / Fall 2024

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