Stanford CS224W: Machine Learning with Graphs - Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 6.3 - Deep Learning for Graphs

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Jure Leskovec Machine Learning Machine Learning with Graphs Graphs CS224W Stanford Deep Learning for Graphs architecture of graph neural networks graph neural networks node embeddings GNNs optimization

Full Transcript

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/

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