For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3vO3Ws3

Jure Leskovec
Computer Science, PhD

In this lecture, we’ll give you an introduction of architecture of graph neural networks. One key idea covered in the lecture is that in GNNs, we’re generating node embeddings based on local network neighborhood. Instead of single layer, GNNs usually consist of arbitrary number of layers to integrate information from even larger contexts. We then introduce how we use GNNs to solve the optimization problems, and its powerful inductive capacity.

To follow along with the course schedule and syllabus, visit:
http://web.stanford.edu/class/cs224w/