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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-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.

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 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.

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 [slides]
Thu 1/12	2. Feature Engineering for ML in Graphs [slides]	Efficient Graphlet Kernels for Large Graph Comparison Weisfeiler-lehman Graph Kernels	Colab 0, Colab 1 out
Tue 1/17	3. Node Embeddings [slides]	DeepWalk: Online Learning of Social Representations node2vec: Scalable Feature Learning for Networks Network Embedding as Matrix Factorization
Thu 1/19	4. Graph Neural Networks [slides]	Geometric Deep Learning: the Erlangen Programme of ML Semi-Supervised Classification with Graph Convolutional Networks	Homework 1 out
Tue 1/24	5. A General Perspective on GNNs [slides]	Design Space of Graph Neural Networks Inductive Representation Learning on Large Graphs Graph Attention Networks
Thu 1/26	6. GNN Augmentation and Training [slides]	Hierarchical Graph Representation Learning with Differentiable Pooling	Colab 2 out	Colab 1 due
Tue 1/31	7. Theory of Graph Neural Networks [slides]	How Powerful Are Graph Neural Networks?
Thu 2/2	8. Label Propagation on Graphs [slides]	Combining Label Propagation and Simple Models Out-performs Graph Neural Networks Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification	Homework 2 out LaTeX template	Homework 1 due
Tue 2/7	9. Machine Learning with Heterogeneous Graphs [slides]	Modeling Relational Data with Graph Convolutional Networks Heterogeneous Graph Transformer		Project Proposal due
Thu 2/9	10. Knowledge Graph Embeddings [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 2/14	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 2/16	12. Fast Neural Subgraph Matching and Counting [slides]	Network Motifs: Simple Building Blocks of Complex Networks Neural Subgraph Matching SPMiner: Frequent Subgraph Mining by Walking in Order Embedding Space	Homework 3 out LaTeX template
Sat 2/18	No class			Homework 2 due
Tue 2/21	13. 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
Thu 2/23	14. 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	Colab 4 out	Colab 3 due
Tue 2/28	15. Advanced Topics in GNNs [slides]	Position-aware Graph Neural Networks Identity-aware Graph Neural Networks Adversarial Attacks on Neural Networks for Graph Data
Thu 3/2	16. Scaling Up GNNs to Large Graphs Guest Lecture: Weihua Hu [slides]	Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks Simplifying Graph Convolutional Networks	Colab 5 out	Homework 3 due
Tue 3/7	Exam
Thu 3/9	17. Geometric Graph Learning Guest Lecture: Minkai Xu [slides]	SchNet: A continuous-filter convolutional neural network for modeling quantum interactions Equivariant message passing for the prediction of tensorial properties and molecular spectra Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation		Colab 4 due
Tue 3/14	18. Trustworthy Graph AI Guest Lecture: Rex Ying [slides]	LIME: Local Interpretable Model-Agnostic Explanations A Unified Approach to Interpreting Model Predictions GNNExplainer Explainability in Graph Neural Networks: A Taxonomic Survey Trustworthy Graph Neural Networks GraphFramEx		Colab 5 due
Thu 3/16	19. Conclusion [slides]
Tue 3/21	No class			Project report due