Deep Learning for Graphs
- Course website: http://web.stanford.edu/class/cs224w/
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.