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Found 2 papers, 1 papers with code
CATGNN: Cost-Efficient and Scalable Distributed Training for Graph Neural Networks
Existing distributed systems load the entire graph in memory for graph partitioning, requiring a huge memory space to process large graphs and thus hindering GNN training on such large graphs using commodity workstations.
Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass
In particular, unlike the traditional GNNs that are trained based on the entire graph in a full-batch manner, recent GNNs have been developed with different graph sampling techniques for mini-batch training of GNNs on large graphs.
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