Feature Engineering

Node Embeddings

Machine Learning with Heterogeneous Graphs

GNNs for Recommender Systems

Geometric Graph Learning

Graph neural network (GNN)

Theory of Graph Neural Networks

Reasoning over Knowledge Graphs

Advanced Topics in GNNs

Label Propagation on Graphs

Fast Neural Subgraph Matching and Counting

Scaling Up GNNs to Large Graphs

GNN Augmentation and Training

Knowledge Graph Embeddings

Deep Generative Models for Graphs

Trustworthy Graph AI

CS 224W: Machine Learning with Graphs Stanford University Winter 2023 The course focuses on the analysis of large graphs and uses machine learning to gain insights into social, technological, and biological systems. Topics include Graph Neural Networks, influence maximization, disease outbreak detection, and social network analysis. ## 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.  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](https://www.cs.mcgill.ca/~wlh/grl_book/) by William L. Hamilton
- [Networks, Crowds, and Markets: Reasoning About a Highly Connected World](http://www.cs.cornell.edu/home/kleinber/networks-book/) by David Easley and Jon Kleinberg

## Previous Offerings

You can access slides and project reports of previous versions of the course on our archived websites: [CS224W: Fall 2021](http://snap.stanford.edu/class/cs224w-2021) / [CS224W: Winter 2021](http://snap.stanford.edu/class/cs224w-2020) / [CS224W: Fall 2019](http://snap.stanford.edu/class/cs224w-2019) / [CS224W: Fall 2018](http://snap.stanford.edu/class/cs224w-2018) / [CS224W: Fall 2017](http://snap.stanford.edu/class/cs224w-2017) / [CS224W: Fall 2016](http://snap.stanford.edu/class/cs224w-2016) / [CS224W: Fall 2015](http://snap.stanford.edu/class/cs224w-2015) / [CS224W: Fall 2014](http://snap.stanford.edu/class/cs224w-2014) / [CS224W: Fall 2013](http://snap.stanford.edu/class/cs224w-2013) / [CS224W: Fall 2012](http://snap.stanford.edu/class/cs224w-2012) / [CS224W: Fall 2011](http://snap.stanford.edu/class/cs224w-2011) / [CS224W: Fall 2010](http://snap.stanford.edu/class/cs224w-2010)
- [Network Science](http://networksciencebook.com) by Albert-László Barabási

CS 224W: Machine Learning with Graphs

Machine Learning

Feature engineering

Feature Engineering

1 courses cover this concept

CS 224W: Machine Learning with Graphs