Curriculum
- 1 Section
- 47 Lessons
- 10 Weeks
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- ML with Graphs47
- 1.1Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.1 – Why Graphs
- 1.2Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.2 – Applications of Graph ML
- 1.3Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.3 – Choice of Graph Representation
- 1.4Stanford CS224W: ML with Graphs | 2021 | Lecture 2.1 – Traditional Feature-based Methods: Node
- 1.5Stanford CS224W: ML with Graphs | 2021 | Lecture 2.2 – Traditional Feature-based Methods: Link
- 1.6Stanford CS224W: ML with Graphs | 2021 | Lecture 2.3 – Traditional Feature-based Methods: Graph
- 1.7Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 3.1 – Node Embeddings
- 1.8Stanford CS224W: ML with Graphs | 2021 | Lecture 3.2-Random Walk Approaches for Node Embeddings
- 1.9Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 3.3 – Embedding Entire Graphs
- 1.10Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 4.1 – PageRank
- 1.11Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 4.2 – PageRank: How to Solve?
- 1.12Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 4.3 – Random Walk with Restarts
- 1.13Stanford CS224W: ML with Graphs | 2021 | Lecture 4.4 – Matrix Factorization and Node Embeddings
- 1.14Stanford CS224W: Machine Learning w/ Graphs I 2023 I Graph Neural Networks
- 1.15Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 7.1 – A general Perspective on GNNs
- 1.16Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 7.2 – A Single Layer of a GNN
- 1.17Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 7.3 – Stacking layers of a GNN
- 1.18Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 8.1 – Graph Augmentation for GNNs
- 1.19Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 8.2 – Training Graph Neural Networks
- 1.20Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 8.3 – Setting up GNN Prediction Tasks
- 1.21Stanford CS224W: ML with Graphs | 2021 | Lecture 9.1 – How Expressive are Graph Neural Networks
- 1.22Stanford CS224W: ML with Graphs | 2021 | Lecture 9.2 – Designing the Most Powerful GNNs
- 1.23Stanford CS224W: Machine Learning w/ Graphs I 2023 I Label Propagation on Graphs
- 1.24Stanford CS224W: Machine Learning w/ Graphs I 2023 I Machine Learning with Heterogeneous Graphs
- 1.25Stanford CS224W: Machine Learning w/ Graphs I 2023 I Knowledge Graph Embeddings
- 1.26Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 11.1 – Reasoning in Knowledge Graphs
- 1.27Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 11.2 – Answering Predictive Queries
- 1.28Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 11.3 – Query2box: Reasoning over KGs
- 1.29Stanford CS224W: ML with Graphs | 2021 | Lecture 12.1-Fast Neural Subgraph Matching & Counting
- 1.30Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 12.2 – Neural Subgraph Matching
- 1.31Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 12.3 – Finding Frequent Subgraphs
- 1.32Stanford CS224W: ML with Graphs | 2021 | Lecture 13.1 – Community Detection in Networks
- 1.33Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 13.2 – Network Communities
- 1.34Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 13.3 – Louvain Algorithm
- 1.35Stanford CS224W: ML with Graphs | 2021 | Lecture 13.4 – Detecting Overlapping Communities
- 1.36Stanford CS224W: Machine Learning w/ Graphs I 2023 I GNNs for Recommender Systems
- 1.37Stanford CS224W: ML with Graphs | 2021 | Lecture 15.1 – Deep Generative Models for Graphs
- 1.38Stanford CS224W: ML with Graphs | 2021 | Lecture 15.2 – Graph RNN: Generating Realistic Graphs
- 1.39Stanford CS224W: ML with Graphs | 2021 | Lecture 15.3 – Scaling Up & Evaluating Graph Gen
- 1.40Stanford CS224W: ML with Graphs | 2021 | Lecture 15.4 – Applications of Deep Graph Generation
- 1.41Stanford CS224W: Machine Learning w/ Graphs I 2023 I Advanced Topics in GNNs
- 1.42Stanford CS224W: ML with Graphs | 2021 | Lecture 17.1 – Scaling up Graph Neural Networks
- 1.43Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 17.2 – GraphSAGE Neighbor Sampling
- 1.44Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 17.3 – Cluster GCN: Scaling up GNNs
- 1.45Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 17.4 – Scaling up by Simplifying GNNs
- 1.46Stanford CS224W: Machine Learning w/ Graphs I 2023 I Geometric Graph Learning, Minkai Xu
- 1.47Stanford CS224W: Machine Learning w/ Graphs I 2023 I Trustworthy Graph AI, Rex Ying
https://www.youtube.com/watch?v=isI_TUMoP60
Stanford CS224W: Machine Learning w/ Graphs I 2023 I Knowledge Graph Embeddings
To follow along with the course, visit the course website:
https://snap.stanford.edu/class/cs224w-2023/
Jure Leskovec
Professor of Computer Science at Stanford University
https://profiles.stanford.edu/jure-leskovec
Learn more and enroll:
Graduate Course: https://online.stanford.edu/courses/cs224w-machine-learning-graphs
Professional Course: https://online.stanford.edu/courses/xcs224w-machine-learning-graphs
To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu