Improving the Long-Range Performance of Gated Graph Neural Networks

Denis Lukovnikov; Jens Lehmann; Asja Fischer

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arXiv · 2007.09668

Improving the Long-Range Performance of Gated Graph Neural Networks

Denis Lukovnikov·Jens Lehmann·Asja Fischer

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Abstract

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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Denis Lukovnikov, Jens Lehmann, Asja Fischer. 2020-07-19. Improving the Long-Range Performance of Gated Graph Neural Networks. https://arxiv.org/abs/2007.09668

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