Stanford University · Course
Machine Learning with Graphs (CS224W)
Machine Learning with Graphs (CS224W) on Stanford Online is a ten-week graduate course on the computational and modelling challenges of analysing massive graphs, from graph neural networks to knowledge graphs and social networks.
Who it suits
- Engineers and analysts working with networks, social data, knowledge graphs or relationship-heavy data.
- Learners who want representation learning and graph neural networks in a credit-bearing course.
What it covers
- Representation learning and graph neural networks.
- Algorithms for the World Wide Web and reasoning over knowledge graphs.
- Influence maximisation, disease outbreak detection and social network analysis.
Before you start
- Stanford lists a conferred bachelor’s degree with an undergraduate GPA of 3.0 or better.
- Computer science principles sufficient to write a non-trivial program.
- Familiarity with basic probability theory and basic linear algebra.
Common questions about Machine Learning with Graphs (CS224W)
- How long is it?
- Stanford lists 10 weeks at 10 to 20 hours a week, for 4 academic units.
- Which programmes count it?
- Stanford lists it toward the Mining Massive Data Sets and Artificial Intelligence graduate certificates.
- Can I enrol any time?
- Enrolment is only open during set periods, and an application is needed for graduate courses, so check the page for the current dates.
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Where this comes from
Written from Stanford University’s own pages, read on 2026-10-14. Content, prices and availability change, so the provider’s page is the source. We are not affiliated with Stanford University and this is not an endorsement.