CS224W: Machine Learning with Graphs | 2021 | Lecture & Knowledge Graph Embedding

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: To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: To view all online courses and programs offered by Stanford, visit: ​