CS224W - Machine Learning with Graphs (Winter 2021, Stanford Univ.) | Computer Science

CS224W - Machine Learning with Graphs

CS224W: Machine Learning with Graphs. Instructor: Prof. Jure Leskovec, Department of Computer Science, Stanford University. 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. You can find more information about this course, such as lecture slides and syllabus, here. (from Stanfordonline)

Introduction: Machine Learning for Graphs

Lecture 01 - Introduction: Machine Learning for Graphs

Lecture 02 - Traditional Methods for ML on Graphs

Lecture 03 - Lode Embeddings

Lecture 04 - Link Analysis: PageRank

Lecture 05 - Label Propagation for Node Classification

Lecture 06 - Graph Neural Networks 1: GNN Model

Lecture 07 - Graph Neural Networks 2: Design Space

Lecture 08 - Applications of Graph Neural Networks

Lecture 09 - Theory of Graph Neural Networks

Lecture 10 - Knowledge Graph Embeddings

Lecture 11 - Reasoning over Knowledge Graphs

Lecture 12 - Frequent Subgraph Mining with GNNs

Lecture 13 - Community Structure in Networks

Lecture 14 - Traditional Generative Models for Graphs

Lecture 15 - Deep Generative Models for Graphs

Lecture 16 - Advanced Topics on GNNs

Lecture 17 - Scaling Up GNNs

Lecture 18 - GNNs in Computational Biology

Lecture 19 - GNNs for Science

References

CS224W: Machine Learning with Graphs (Winter 2021)
Instructor: Prof. Jure Leskovec. Lecture Slides. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs.