Stanford CS224W: Machine Learning with Graphs - Stanford CS224W: ML with Graphs | 2021 | Lecture 10.1-Heterogeneous & Knowledge Graph Embedding

4.0 (2)

29 learners

What you'll learn

This course includes

22.3 hours of video
Certificate of completion
Access on mobile and TV

Summary

Keywords

Jure Leskovec Machine Learning Machine Learning with Graphs Graphs CS224W Stanford Heterogeneous Graphs Knowledge Graph Embeddings RGCN GCN labels of nodes block diagonal matrices

Full Transcript

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pNkBLE Lecture 10.1 - Heterogeneous Graphs and Knowledge Graph Embeddings Jure Leskovec Computer Science, PhD In this lecture, we first introduce the heterogeneous graph with the definition and several examples. In the next, we talk about a model called RGCN which extends the GCN to heterogeneous graph. To make the model more scalable, several approximated approaches are introduced, including block diagonal matrices and basis learning. At last, we show how RGCN predicts the labels of nodes and links. To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224w/

Continue this lesson in the app

Install CourseHive on Android or iOS to keep learning while you move.

Welcome