Training a GNN by querying a graph database for neighbor samples and features reduces memory use enough to train on small machines, but is much slower than in-memory training.
Layer- dependent importance sampling for training deep and large graph convolutional networks
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Graph Neural Networks on Graph Databases
Training a GNN by querying a graph database for neighbor samples and features reduces memory use enough to train on small machines, but is much slower than in-memory training.