Stanford CS224W: ML with Graphs | 2021 | Lecture 17.1 - Scaling up Graph Neural Networks

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

Jure Leskovec
Computer Science, PhD

In real-world applications, such as recommendation systems and social networks, graphs can be very large with millions if not billions of nodes and edges. This makes the native full batch GNN training and testing extremely hard as the GPU memory is limited. In this lecture, we will introduce three methods that scale up GNNs: 1) Neighbor Sampling, 2) Cluster-GCN, and 3) Simplified GCN.

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

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