CS224 W: Machine Learning with Graphs, 2021, Lecture 17. 2 Graph SAGE 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 Klayer GNN generates a node embedding by using only the nodes from the Khop neighborhood around that node. Therefore, to generate embeddings of nodes in the minibatch, only the Khop 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 Stanfords upcoming professional programs in Artificial Intelligence, visit: To view all online courses and programs offered by Stanford, visit:
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