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Improving the Long-Range Performance of Gated Graph Neural Networks
Denis Lukovnikov, Jens Lehmann, Asja Fischer · 2020-07-19 · via cs.LG updates on arXiv.org

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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