Shypd.aiThe largest open index of AI toolsGraphSAINT is an open-source framework for training Graph Neural Networks (GNNs) on large graphs. It offers fast and accurate minibatch training using a graph sampling-based inductive learning method.
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GraphSAINT is an open-source framework designed for efficient and accurate training of Graph Neural Networks (GNNs) on large-scale graphs. It introduces a novel minibatch method that samples small subgraphs from the full training graph, allowing for complete GNN construction and propagation on these subgraphs without further layer sampling. This approach addresses the 'neighbor explosion' problem common in other methods, leading to linear computation cost with GNN depth and improved scalability. GraphSAINT supports various GNN architectures like GraphSAGE, GAT, JK-Net, GaAN, and MixHop, and offers multiple graph samplers including Node, Edge, RW, MRW, and Full graph. It provides implementations in both TensorFlow and PyTorch, making it flexible for researchers and developers working with deep GNNs.
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