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Improving the Long-Range Performance of Gated Graph Neural Networks

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 نشر من قبل Denis Lukovnikov
 تاريخ النشر 2020
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Many popular variants of graph neural networks (GNNs) that are capable of handling multi-relational graphs may suffer from vanishing gradients. In this work, we propose a novel GNN architecture based on the Gated Graph Neural Network with an improved ability to handle long-range dependencies in multi-relational graphs. An experimental analysis on different synthetic tasks demonstrates that the proposed architecture outperforms several popular GNN models.



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