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- W4327652214 abstract "We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs. The advantages of our framework are two-fold; 1) it transforms various non-scalable GNNs to scale well to large-scale graphs by separating local feature aggregation from weight learning in their graph convolution, 2) it efficiently executes precomputation on GPU for large-scale graphs by decomposing their edges into small disjoint and balanced sets. Through extensive experiments with large-scale graphs, we demonstrate that the transformed GNNs run faster in training time than existing GNNs while achieving competitive accuracy to the state-of-the-art GNNs. Consequently, our transformation framework provides simple and efficient baselines for future research on scalable GNNs." @default.
- W4327652214 created "2023-03-18" @default.
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- W4327652214 date "2023-01-01" @default.
- W4327652214 modified "2023-09-24" @default.
- W4327652214 title "GNN Transformation Framework for Improving Efficiency and Scalability" @default.
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- W4327652214 doi "https://doi.org/10.1007/978-3-031-26390-3_22" @default.
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