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

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/