CS224W: Machine Learning with Graphs | 2021 | Lecture 17.2 - GraphSAGE Neighbor Sampling

Lecture 17.2 - GraphSAGE Neighbor Sampling Scaling up GNNs Jure Leskovec Computer Science, PhD Neighbor Sampling is a representative method used to scale up GNNs to large graphs. The key insight is that a K-layer GNN generates a node embedding by using only the nodes from the K-hop neighborhood around that node. Therefore, to generate embeddings of nodes in the mini-batch, only the K-hop neighborhood nodes and their features are needed to load onto a GPU, a tractable operation even if the original graph is large. To further reduce the computational cost, only a subset of neighboring nodes is sampled for GNNs to aggregate. To follow along with the course schedule and syllabus, visit: To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: To view all online courses and programs offered by Stanford, visit: ​