Corpus ID: 221516279

GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training

@article{Cai2020GraphNormAP,
  title={GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training},
  author={Tianle Cai and Sheng-Jie Luo and Keyulu Xu and Di He and T. Liu and Liwei Wang},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.03294}
}
  • Tianle Cai, Sheng-Jie Luo, +3 authors Liwei Wang
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Normalization plays an important role in the optimization of deep neural networks. While there are standard normalization methods in computer vision and natural language processing, there is limited understanding of how to effectively normalize neural networks for graph representation learning. In this paper, we propose a principled normalization method, Graph Normalization (GraphNorm), where the key idea is to normalize the feature values across all nodes for each individual graph with a… CONTINUE READING
    2 Citations

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