Winter 2023
Stanford University
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.
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:
The recitation sessions in the first weeks of the class will give an overview of the expected background.
No data.
Notes and reading assignments will be posted periodically on the course Web site. The following books are recommended as optional reading:
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