Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 17.3 – Cluster GCN: Scaling up GNNs

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Jure Leskovec
Computer Science, PhD

Neighbor Sampling, presented in the previous lecture (17.2) constructs a computational graph separately for each node in a mini-batch. This creates a lot of redundancy in computing node embeddings within the mini-batch. A different approach is to sample a subgraph from a large graph that is small enough to be loaded into GPU. Then, the efficient and non-redundant full-batch GNN can be applied over the sampled subgraph. An example of this method is Cluster-GCN. Cluster-GCN first pre-processes a large graph by partitioning it into clusters of nodes. Then, during training, it samples clusters of nodes in each mini-batch and applies the full-batch GNN over the induced subgraph.

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