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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.
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