GNN Transformation Framework for Improving Efficiency and Scalability

• URL: http://arxiv.org/abs/2207.12000v1
• Date: Mon, 25 Jul 2022 09:19:59 GMT
• Title: GNN Transformation Framework for Improving Efficiency and Scalability
• Authors: Seiji Maekawa, Yuya Sasaki, George Fletcher, Makoto Onizuka
• Abstract summary: 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.
• Score: 5.833671647960204
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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